The sunspot number associated with solar activity was analyzed to investigate the relationship between the stability of rDCB and changes in the ionospheric TEC.

Figure 1 shows the daily sunspot numbers during the period from 2011 to 2016.

Data on sunspot numbers is available at http: //www.sidc.be/silso/ssngraphics.

In Figure 1, the yellow solid line indicates the daily sunspot number and the red solid line represents the smoothed daily sunspot number with sinusoidal harmonic curve fitting.

Within solar cycle 24, solar activity reached its maximum in April 2014 and has weakened in the number of sunspots since then.

In the present study, we consider ionospheric TEC changes and rDCB stability in a period of weakening solar activity from 2014 to 2016.


Figure 2 shows the locations of the six GNSS reference stations selected to investigate the relationship between ionospheric TEC and rDCB, and between ionospheric TEC and rDCB stability.

The locations of GNSS reference stations are considered in terms of three latitude zones: low latitude (20 • S-20 • N), mid-latitude (20 • S-60 • S, 20 • N-60 • N) and high latitude (60 • S-90 • S, 60 • N-90 • N), respectively.

There are two GNSS stations in each latitude zone.

Table 1 shows the receiver and antenna types at the six GNSS reference stations from 2014 to 2016.

The IGS network consists of more than 500 GNSS stations distributed around the world.

All selected stations in this study are included in the IGS network.

We used two major considerations when selecting these GNSS stations.

First, the GNSS receivers and antennas should not be replaced during the three years from 2014 to 2016.

Second, the DCBs should be reliably provided with daily values in GIM products.

In addition, different GNSS receiver models were considered to exclude receiver-type dependency.

Other GNSS sites, except for NKLG and WILL, are equipped with different receivers as listed in Table 1.


Figure 3 shows the time series of vertical TEC units (VTEC) (top panel), rDCB (middle panel), and rDCB RMS (bottom panel) for each GNSS station from 2014 to 2016.

The amplitude of the VTEC values at all stations (DGAR, NKLG, GRAS, WILL, MAW1, and THU2) decreased gradually with solar activity.

The rDCB RMS values are multiplied by the number 50 because their values are too small.

Therefore, we can see clear variations in the rDCB RMS*50 time series.


Figure 3a,b show the time-dependent changes of the above quantitative variables at the DGAR and NKLG stations, which are located at the Earth's equatorial region.

The data curves at mid-latitudes (GRAS and WILL) and higher latitudes (MAW1 and THU2) are shown in Figure 3c-f.


The most dominant feature in the VTEC, rDCB, and rDCB RMS*50 time series is the annual frequency component.

A strong semiannual component is also observed in low- and mid-latitude regions.

Zhao et al. [ 27] investigated TEC climatology from the global ionosphere maps; they reported that the semiannual behavior of the ionospheric TEC is more significant in low-latitude regions than in high-latitude regions, which is consistent with our results.


As a matter of fact, the amplitude of VTEC at MAW1 in the Southern Hemisphere is higher than that at THU2 in the Northern Hemisphere.

Therefore, at high-latitudes, the rDCB does not seem to be significantly correlated with TEC variations.


A polynomial fitting method with 10 degrees is considered to show clear periodic features in the data time series.

As shown in Figure 3, the magnitude of the VTEC is normally larger in low-latitude regions, while it is smaller in higher latitude regions.

The effect of solar activity on ionospheric TEC can be stronger at low latitudes than at mid- or high-latitudes.

At all stations, periodic variations in the rDCB and rDCB RMS*50 time series are similar to changes in the VTEC time series.


To investigate the relationship between the two different variables, a Pearson correlation was considered.

The Pearson correlation is the most widely used to find the linear relationship between normally distributed variables.

Therefore, Pearson's coefficient was used to calculate the linear relationship between two variables according to the following Equation (3).


where µA and σA are the mean and standard deviation of the variableA, respectively.

µB and σB are the mean and standard deviation of the variable B. The index, R, which indicates the correlation between the two variables, is calculated by the following Equation (4).


Since the variables A and B always have a direct correlation to themselves, the diagonal elements ρ(A, A) and ρ(B, B) of the matrix are necessarily equal to one.

R is simply defined as ρ(A, B).

Several approaches have been suggested to interpret the correlation coefficient with respect to different cutoff points.

In this study, we choose a classification proposed by Schober et al. [ 28].

They categorised the absolute magnitude of the correlation coefficient into five categories as follows: 0.00-0.10 (negligible correlation), 0.10-0.39 (weak correlation), 0.40-0.69 (moderate correlation), 0.70-0.89 (strong correlation), and 0.90-1.00 (very strong correlation).


Figure 4 shows different correlation coefficients between rDCB and VTEC, and between rDCB RMS and VTEC, respectively.

In Figure 4a-f, the upper panels show the correlation between rDCB and VTEC, while the lower panels show the correlation between rDCB RMS and VTEC.

The value of R between rDCB and VTEC ranges from 0.33 to 0.78, while R between rDCB RMS and VTEC varies from 0.50 to 0.94.


Two GNSS stations (DGAR and NKLG) in the equatorial regions showed a positive linear relationship between rDCB and VTEC.

Their R values were 0.33 and 0.40, respectively.

According to the classification defined by Schober et al. [ 28], the DGAR and NKLG stations can be interpreted as having a weak or moderate positive correlation between the two variables.


On the other hand, rDCB RMS*50 and VTEC are highly correlated.

As shown in Figure 4a,b, the R values for this correlation are 0.87 and 0.94, respectively.

In mid-latitude regions (GRAS and WILL), the R values between VTEC and rDCB are 0.78 and 0.73, respectively.

This indicates that there is a strong correlation between the two variables in mid-latitude regions.

In addition, the relationship between VTEC and rDCB RMS*50 at mid-latitudes was investigated.

The R values for this correlation were 0.60 and 0.68, respectively.

We can see that there is a clear difference between these variables in the low-latitude and mid-latitude regions.


The correlation between ionospheric TEC and rDCB stability in low-latitude regions is higher than that in mid-latitude regions.

Conversely, the correlation between ionospheric TEC and rDCB is relatively higher in mid-latitude regions.

To investigate the latitudinal dependency of the correlation in more detail, we considered two additional GNSS stations (MAW1 and THU2) in high-latitude regions.

As shown in Figure 4e,f, the two stations show different correlations.

At MAW1 station in the southern hemisphere, the correlation between VTEC and rDCB was slightly smaller than that between VTEC and rDCB RMS.

In contrast, the THU2 site in the highest latitude of the northern hemisphere, the correlation (R~0.64) between VTEC and rDCB was a little larger than that (R~0.50) between VTEC and rDCB RMS.


As a result, correlations between VTEC and rDCB RMS are closely associated with a latitudinal dependency.

R between VTEC and rDCB is the smallest at low-latitude regions.

The mid-latitude regions exhibit the maximum value of R. Ionospheric TEC variability in mid-latitude is generally less irregular compared to that in low and high latitudes.

The estimation of rDCB at mid-latitude is also expected to be stable.

It can mainly be associated with the effect of ionospheric irregularities like rapid plasma fluctuation, which is known as ionospheric scintillation.

Ionospheric irregularities occur more often in low- and high-latitudes than in mid-latitudes [29].

They can affect the amplitude and phase of GNSS signals rapidly and seem to degrade the accuracy of rDCB.

Therefore, the correlation between VTEC and rDCB itself at mid-latitude may be relatively higher than that at low and high latitudes.

On the other hand, R between VTEC and rDCB RMS*50 becomes smaller on average in high-latitude regions.

The peak value of R is found in low-latitude regions.

The latitude-dependent tendency for these correlations is clearly seen in the bottom panels of Figure 4a-f.


To further analyze the latitudinal dependency of the correlation between VTEC and rDCB RMS in more detail, data from the 7 additional GNSS stations were examined.

The receiver and antenna types at the 7 additional GNSS stations from 2014 to 2016 are listed in Table 2.

Figure 5 shows the locations of the 13 GNSS stations and the R values between VTEC and rDCB RMS for three years.

The numbers in parentheses in Figure 5 represent the R values.

We can also see the apparent latitudinal dependency from the R values of the 13 GNSS stations.


The three GNSS stations (NKLG, DGAR, and DARW) at low latitudes have large R values above 0.86.

In particular, as the latitude increases from the equator, the R value decreases slightly.

There are 6 GNSS stations (WILL, GRAS, JPLM, ULAB, SUWN, and CORD) in mid-latitude regions.

Their R values range from 0.6 to 0.8.

The averaged value of R is approximately 0.69, which is smaller than that on average in low-latitude regions.

The four GNSS stations (THU2, SCOR, MAW1, and CAS1) at high latitudes have weak correlations compared to those at low and mid-latitudes.

The R value ranges from 0.49 to 0.61.


Interestingly, the R values in high-latitudes of the Southern Hemisphere were greater than those in high-latitudes of the Northern Hemisphere.

R between VTEC and rDCB RMS can be associated with the magnitude of TEC following the latitudinal dependency.

As shown in Figure 3e-f, the magnitude of TEC is higher at the Southern Hemisphere compared to the Northern Hemisphere during three years from 2014 to 2016.

Therefore, the asymmetry of the ionosphere in different hemispheres can affect R between VTEC and rDCB RMS.


The ionosphere over equatorial and low-latitude regions can be strongly influenced by phenomena such as daytime spread-F, equatorial ionization anomalies, and intense irregularities in TEC distributions.

These effects in the equatorial and low-latitude regions can affect GPS signals passing through the ionosphere.

In particular, the dynamically changing ionosphere in the equatorial regions may have a large effect on the stability of the receiver DCB.

Therefore, a strong ionospheric activity might induce a high correlation between TEC and receiver DCB stability.

In high-latitude regions, however, the intense ionospheric TEC irregularities are presumed to be caused by auroral activities and high-speed plasma convection [30].

The magnitude of TEC at high latitudes is about three times smaller than that at low latitudes [31].

The correlation between TEC and rDCB in low-latitude regions is smaller than that in high-latitude regions.

On the other hand, the amplitude of TEC decreases with higher latitudes.

This may also be associated with rapid TEC changes, or to the magnitude of TEC.


There was a high correlation between TEC and rDCB stability at low latitudes.

For a better analysis of these characteristics, we considered two GNSS stations (BOGT and KOUR) located in South America, as shown in Figure 6.

The geographic latitudes of BOGT and KOUR stations at the equatorial region are about 4.64 and 5.25 degrees, respectively.

For three years from 2014 to 2016, the BOGT and KOUR sites had operated GNSS receivers (JAVAD TRE_3 DELTA and SEPT POLARX4) of different type models.

The rDCB may be different depending on the receiver type.

Even the same receiver models have different rDCB values [18,30].

Therefore, it may be important to investigate the correlation in the GNSS stations with different receiver models.

Figure 6b shows the time series of the VTEC and rDCB RMS*50 at BOGT (top panel) and KOUR (lower panel), respectively.

The blue solid line denotes the changes in VTEC for three years from 2014 to 2016.

Purple circles represent the changes in the rDCB RMS*50.

The linear correlation between those variables is displayed in the upper right corner of each panel.


The VTEC values have been decreasing gradually since 2014, as seen in the upper panel of Figure 6b.

The maximum value of the TEC peaked in 2014, and reached its minimum value at the end of 2016.

We can also observe a clear annual and semi-annual variation of the VTEC.

The VTEC was higher at the equinoxes than at the solstices during the period from 2014 to 2016.

The magnitude of TEC was highest at the March equinox during this period, and lowest at the June solstice.


This is fully consistent with the results reported by Akala et al. [ 32].

They attribute to increasing in ion production rate in the equinox and low ionization resulting from reduced production rate in the solstice during this period.


As shown in the upper panel of Figure 6b, the changes in rDCB RMS were very similar to the changes in VTEC.

We calculated that the value of R between VTEC and rDCB RMS was about 0.90.

From the results in the bottom panel of Figure 5b, the KOUR station with R of 0.91 shows very similar features to the BOGT station.

Changes in VTEC could be similar because the two stations have similar latitudes and are located close to each other.

It is noted that although the two stations use different GNSS receiver models, the changes in rDCB RMS are similar.

This suggests that the ionospheric TEC at low latitudes and equatorial regions is strongly related to the stability of the rDCB.


In addition, we have analyzed the changes in rDCB and rDCB RMS as the model of the GNSS receiver is changed.

Figure 7 shows the time series of rDCB and rDCB RMS at the ALIC GNSS station (GLAT: 23.7 • S, GLON: 133.8 • E) from 2014 to 2016.

The red solid lines represent the polynomial-fitted curve of the data.


As shown in the upper panel of Figure 7, the rDCB values have been stable since 2014 with only small changes.

However, they show a large discrepancy in early 2016.

This is due to the replacement of the GNSS receiver.

In Figure 7, the blue vertical dotted line indicates when the receiver was replaced.

The large change in the rDCB is exactly in line with receiver replacement.

The GNSS receiver in the ALIC station was changed from the 'LEICA GRX1200GGPRO' to the 'LEICA GR25 model in February 2016.

The rDCB value of the ALIC station changed from about 12 to 21 ns with the receiver replacement.


The lower panel of Figure 7 shows the rDCB RMS time series for three years.

The rDCB RMS reaches its maximum value in early 2014, and then tends to decrease gradually.

In addition, rDCB RMS has periodic and time-varying features.

It is noteworthy that the rDCB RMS of the ALGO station did not change significantly despite the replacement of the receiver in early 2016.

Changes in the rDCB RMS, even after receiver replacement, have continuity.

This indicates that the stability of the rDCB can also be independent of the GNSS receiver model.


To investigate the correlation between VTEC changes and rDCB stability before and after replacing the receiver, we plotted the time series of VTEC and rDCB RMS*50 for the same period of time.

In Figure 8, the blue solid line and the brown solid line represent the VTEC and rDCB RMS changes, respectively.

They represent a polynomial fitting of the data.

The time series of the two variables show similar features, such as periodicity and damping.

R between the two variables is about 0.92, which means that there is a very strong correlation.

Therefore, we found that the ionospheric TEC and rDCB stability have a very strong correlation even after the replacement of the receiver.


According to Kao et al. [ 33], there are various factors affecting GNSS rDCB estimation accuracy.

They demonstrated that the estimation accuracy of rDCB is lower in the low-latitude regions because TEC changes in low-latitude regions are more complicated than those in mid-latitude regions.

In this study, we showed that the values of R between TEC and rDCB RMS were also larger in low-latitude regions than those in mid-latitude or high-latitude regions.

Besides, this may be explained that rDCB stability is poor due to the complex structure of ionospheric TEC in low-latitude regions.

It can affect the accuracy of ionospheric TEC estimation.

Eventually, the estimation of real-rDCB can increase the accuracy of TEC estimation, which will contribute to improving the position accuracy of the GNSS users.


In this study, we have investigated the relationship between VTEC and rDCB, and VTEC and rDCB stability, during the three years from 2014 to 2016.

Pearson's linear correlation values were considered in order to quantify the relationships between the variables.

In addition, because the magnitude of the GNSS TEC has a latitudinal dependency, we used data from 13 GNSS stations located in low-, middle-, and high-latitude regions.


The time series of VTEC, rDCB, and rDCB RMS at each GNSS station from 2014 to 2016 were analyzed.

We could see clear periodic features in time series at different latitudes.

The most prominent features were the annual and semiannual variations.

The semiannual variations were more significant in low-latitude and mid-latitude regions than in high-latitude regions.


In addition, these characteristics of rDCB and rDCB RMS have a similarity with those of VTEC, at all stations.

By calculating R between the variables, it can be shown that the correlation between TEC and rDCB is the smallest in low-latitude regions.

The mid-latitude regions exhibit the maximum value of R. This can be related to ionospheric TEC irregularities with different latitude regions.


In contrast, R values between TEC and rDCB RMS were greater in low-latitude regions, which can be associated with the magnitude of TEC.

A strong positive correlation (≥ 0.90) between TEC and rDCB RMS was also observed at the two additional GNSS stations in low-latitude regions.

This suggests that the correlation between the two different variables has a clear latitudinal dependence.


The rDCB values showed large differences before and after replacing GNSS receivers, which is consistent with earlier studies.

The rDCB RMS has periodic and time-varying features, and is not changed by the replacement of the receiver.

Changes in the rDCB RMS have continuity even after receiver replacement.

R between TEC and rDCB RMS after replacing the receiver was 0.92, indicating a very strong correlation.

Therefore, it can be noted that rDCB RMS is not related to the GNSS receiver model.

As a result, we found that ionospheric TEC and rDCB stability still have a very strong correlation.

Estimation of stable rDCB can contribute to improving the accuracy of the GNSS ionospheric TEC.

Therefore, applying the correlation between ionospheric TEC changes and rDCB stability in different latitude regions to the ionospheric TEC will allow for more accurate monitoring of GNSS ionospheric TEC.


Choi et al. [ 1] analyzed the correlation between the ionospheric total electron content (TEC) and the Global Navigation Satellite System (GNSS) receiver differential code bias (DCB) and concluded that the long-term variations of the receiver DCB are caused by the corresponding variations in the ionosphere.

Unfortunately, their method is problematic, resulting in conclusions that are not useful.

The long-term variations of the Global Positioning System (GPS) DCBs are primarily attributed to the GPS satellite replacement with different satellite block series under the zero-mean constraint condition, rather than the ionospheric variability.


In the ionospheric total electron content (TEC) retrieval, the differential code bias (DCB) caused by the Global Navigation Satellite System (GNSS) satellite transmitter and receiver hardware is one of the main error sources, and it has been widely studied.

Choi et al. [ 1] have recently proposed a new aspect of the correlation between the ionospheric TEC and the GNSS receiver differential code bias (rDCB).

They stated that “the characteristics of rDCB and rDCB root mean square (RMS) have a similarity with those of vertical TEC (VTEC), at all stations”, and they calculated Pearson’s coefficient (R) between the TEC and rDCB and showed that “the correlation between TEC and rDCB is the smallest in low-latitude regions.

The mid-latitude regions exhibit the maximum value of R”.

Supposedly, the GNSS DCB should be related with the transmitter hardware and its environment, as expected.

Studies also showed that the variations of the Global Positioning System (GPS) satellite DCB and receiver DCB are not caused by the ionospheric variability [2,3].

However, Choi et al. [ 1] might have advertently or inadvertently overlooked the work of Zhong et al. [ 2,3].

As discussed subsequently, it does not make sense to utilize the comparison between the ionospheric TEC and the GNSS receiver DCB to demonstrate their cause–effect relationship.

Thus, the correlation analysis between TEC and rDCB as described by Choi et al. [ 1] is not meaningful.


Zhong et al. [ 2,3] have assessed the variations of the GPS satellite DCB and receiver DCB.

Note that in the DCB estimation, only the combined satellite–receiver DCB (the sum of the GPS DCB and receiver DCB) can actually be determined, so that an additional constraint condition is required to further separate the GPS DCB and receiver DCB.

Generally, the zero-mean condition imposed on all satellite DCBs (i.e., the daily mean of all GPS DCBs is set as zero) is introduced for the DCB separation [4].

Figure 1 shows the comparisons of the GPS DCB of Pseudo Random Number (PRN) 13 from the International GNSS Service (IGS) [5] and solar activity proxy F10.7 during 1998–2020.

It seems that the GPS DCB also showed a solar-cycle-like variation [6], if only the period of 2002–2014 is considered.

However, this is not the case for the whole period of 1998–2020.

Figure 2 presents the lifetime of each GPS satellite from 1998 to 2020.

It is clear that the new satellite block series are replacing the old ones gradually.

From the GPS DCB values on 1 January 2015 as displayed at the upper left corner of Figure 2, it is evident that the GPS DCBs from different satellite block series show a significant difference.

Under the zero-mean condition imposed on all GPS DCBs, when a new GPS satellite replaces a decommissioned one with a different block series, the GPS DCB values change relatively to satisfy the zero-mean condition.

Specifically, when a new GPS satellite with higher (lower) DCB values comes into service, all the GPS DCB values descend (increase).

Thus, the long-term variations of the GPS DCBs are not caused by the ionospheric variability, while they are attributed to the GPS satellite replacement with different satellite block series under the zero-mean condition [2].

Correspondingly, the estimated receiver DCB will also change, as the daily combined satellite–receiver DCB is constant.


Note that only after the effect of GPS satellite replacement is removed, the DCB could be used for comparison or periodic spectral analysis.

To remove the effect of GPS satellite replacement, we can reconstruct the DCB value under a new constraint condition imposed only on continuously operating GPS satellites during a specific period.

The new constraint condition for the GPS DCBs is expressed as follows: where DCBs,rec denotes the reconstructed GPS DCB, n is the number of the selected GPS satellites, and C is a constant value.

During the period of 2005–2019, nine continuously operating GPS satellites (PRN 11, 13, 14, 16, 19, 20, 22, 23, and 28) are selected.

For each day, the offset value is calculated based on the mean value of the GPS DCBs of the selected satellites: Then, the reconstructed GPS DCB (DCBs,rec) can be obtained from the original GPS DCB and the offset value as follows: The receiver DCB (DCBr,rec) can be also obtained in the similar way, but with an opposite sign on the offset value: The reconstructed GPS DCB of PRN28 is shown in Figure 3a as an example.

On the long-term scale, the reconstructed GPS DCB only showed a slight variation of less than 1 TECU.

In addition, the reconstructed GPS DCB presented short-term periodic variations, which is less than one year.


The Lomb–Scargle periodograms of the original and reconstructed GPS DCBs are shown in Figure 3b.

It is clear that the original GPS DCB presented multiple periodic components, while some periodic components in the reconstructed GPS DCB became much weaker.

This indicates that DCB reconstruction could filter out the artificial periodic variations.

In addition, it is interesting that there is a prominent periodic component of about 175 days in Figure 3b, which is a little shorter than half of a sidereal year (182.625 days).

Zhong et al. [ 3] demonstrated that the periodic variation of CHAMP satellite receiver DCB is mainly attributed to its hardware thermal status.

Here, we calculated the daily mean solar angle on the zenith side of the GPS satellite to represent its hardware thermal status, as shown in Figure 3c.

The periodogram of the daily mean solar angle in Figure 3d displayed a clear periodic component of about 175 days, which is generally in agreement with that of the GPS DCB.

This period is matched with the period of a GPS draconitic year, which is equal to about 351.5 days.

The GPS draconitic year is the time taken for the Sun to complete one revolution (return to the same point in space) with respect to the GPS orbital nodes, as seen from the Earth [7,8].

In one GPS draconitic year, there are exactly two eclipsing periods, which means that after 175.75 days, the elevation of the Sun above the orbital plane is the same, and the direction of the relative change in the height of the Sun is also the same.

As a result of the Sun illumination and attitude of GPS satellites [9], every 175.75 days the Sun illuminates the same parts (electron circuits) of the GPS satellite in the same manner, which may change the hardware thermal status with the same period and further affect the DCB values.

The periodic component of about 175 days can be seen in DCB around half of the total GPS satellites (not shown), but it is still not clear whether the effect of hardware thermal status on DCB is consistent for different GPS satellite block series.

Many other GNSS-derived parameters are characterized by the draconitic repeatabilities or their harmonics, such as GNSS-based Earth Rotation Parameters [10].

Overall, both the solar-cycle-like and semiannual-like variations of the GPS DCB are artificial and not caused by the ionospheric variability.


Figure 4 shows the original and reconstructed receiver DCBs for station GRAS as an example, as used in Choi et al. [ 1].

Again, the original receiver DCB presented a declined trend from 2014 to 2016, when F10.7 was mainly decreasing.

However, the reconstructed receiver DCB only changed slightly during 2012–2017.

The periodograms showed that the receiver DCB also presents a significant periodic component of about 176 days, which should be associated with the GPS draconitic year, as discussed above.

In addition, there is a significant annual periodic component (365 days).

The short-term variations of the receiver DCB are attributed to various factors, such as the hardware temperature [11].

From the comparison between surface temperature and the reconstructed receiver DCBs from many IGS stations (not shown), it illustrated that when the surface temperature reached its maximum (minimum), some of the reconstructed receiver DCBs also presented the maximum (minimum) values.

Thus, the annual periodic component could probably be associated with the variations of the surface temperature.

However, the manufacturer and model, antenna mounting position, cable construction, and thermal control could be complicated and different for each receiver, so it is difficult to draw a unified conclusion in respect of short-term variations of the receiver DCB.


As discussed above, both the long-term and short-term variations of the GPS DCBs and receiver DCBs are accessed.

Though during specific periods, they were similar to the ionospheric variations by coincidence, they are not caused by the ionospheric variability physically.

In the ionospheric TEC/DCB estimation, it is generally assumed that the ionosphere satisfies the spherically symmetric hypothesis or spherical harmonic expansion distribution.

When the actual ionospheric distributions deviate from the assumption, the TEC measurement used in the DCB determination is subject to a large number of small random errors, and the probability distribution for the total statistical error is driven toward the Gaussian distribution, indicated by higher RMS (lower stability) for DCB.

In TEC/DCB estimation, the larger the ionospheric TEC is, the greater the deviation from the spherically symmetric assumption would be, thus causing higher RMS (lower stability) for DCB, as presented by Choi et al. [ 1].

However, as mentioned above, in the physical sense the ionospheric TEC cannot alter the rDCB, which is essentially subject to the receiver hardware.


In summary, Choi et al. [ 1] concluded arbitrarily that the long-term variations of the receiver DCB are caused by the ionospheric variability on the basis of the problematic method.

They were not aware of the effect of the GPS satellite replacement on the DCB determination.

Overall, the study of Choi et al. [ 1] could introduce confusion and be misleading for the GNSS and the ionospheric communities.


Choi et al. ( 2019) suggested that ionospheric total electron content (TEC) and receiver differential code bias (rDCB) stability have a strong correlation during a period of two years from 2014 to 2016.

This article is a response to Zhong et al. ( 2020), who pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability.

In this issue, we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) products from 2000 to 2020.

In this study, changes in TEC and receiver DCB (rDCB) root mean squares (RMS) at Bogota (BOGT) station still have a clear correlation.

In addition, there was a moderate correlation between satellite DCB RMS and rDCB RMS.

As a result, we suggest that rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.

The Global Navigation Satellite System (GNSS) Differential Code Biases (DCB) is the largest error source in estimating the total electron content (TEC) of the ionosphere.

Many studies have reported that DCB is affected by a variety of factors [1-6].

There can be significant jumps [7] and other features in DCB related to the replacement of the receiver and antenna.


Choi et al. [ 8] showed that ionospheric TEC and receiver DCB (rDCB) root mean squares (RMS) have a strong correlation during a period of two years from 2014 to 2016.

In addition, they focused on the relation between rDCB stability and ionospheric activity rather than rDCB itself.

However, Zhong et al. [ 9] pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability.

Interestingly, they also focused on the GPS DCB changes and did not mention the relationship between rDCB stability and ionospheric activity.

As suggested by Zhong et al. [ 9], rDCB and satellite DCB may have a significant correlation.

In this issue, Choi et al. [ 8] suggested that changes in the rDCB RMS still have continuity after the replacement of a receiver.

Replacing hardware such as GNSS receivers or satellites can seriously affect the DCB.

However, large changes in DCB with hardware replacement may be usually instantaneous.

For more reliable analysis of the results presented by Choi et al. [ 8], we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) data from 2000 to 2020.

Figure 1 shows the time series of vertical TEC units (VTECU), rDCB, and rDCB RMS (Stability) at BOGT station, respectively.

A band-pass filter was applied to isolate specific changes for GIM-TEC and rDCB RMS.


As shown in Figure 1a, the variations of VTEC are closely related to solar activities.

They also show an exact annual and semi-annual cycle.

To analyze the relationship between VTEC and rDCB RMS during this period, we calculated band-pass filtered (0~0.05 Hz) rDCB RMS plotted in Figure 1a as a red solid line.

In CODE’s GIM products, the rDCB RMS values of the BOGT station exist only from 2003 to 2020.

Filtered rDCB RMS showed annual and semi-annual patterns similar to GIM-TEC.

However, there were no regular changes in rDCB RMS from 2006 to 2011, and solar activity was relatively weak during this period.

Nevertheless, changes in VTEC and rDCB RMS appear to have a clear correlation.

Figure 1b shows the time series of GPS rDCB at BOGT station (4.64°N, 74.08°W) from about 2003 to 2020.

A significant jump in rDCB was detected due to receiver replacement.

Changes in rDCB do not show any regular patterns similar to VTEC.

Zhong et al. [ 9] suggested that the variations of rDCB are not caused by the ionospheric variability, which may be consistent with our results.

The blue dashed rectangles “A” and “B” in Figure 1b represent the changes of rDCB in low- and high-ionospheric activity, respectively.

The standard deviations (STD) of rDCB in the two sections “A” and “B” were about 0.48 and 0.80, respectively.

Variations in rDCB can see to be unstable during high ionospheric activity.

On the other hand, they are stable when ionospheric activity is relatively low.

Therefore, rDCB changes can be associated with ionospheric activity.

In addition, as shown in Figure 1c, rDCB RMS values were small and stable during low-ionospheric activity.


Figure 2 presents the time series of GPS satellite P1-P2 DCB (sDCB), band-pass filtered (0~1.5Hz) sDCB, and rDCB RMS from 2000 to 2020, respectively.

Changes in DCB for all GPS satellites are shown in Figure 2a.

In Figure 2a, it is not easy to distinguish specific correlations with ionospheric activity in GNSS sDCB changes.

To analyze the relationship between sDCB and rDCB, we considered the most stable satellites with no PRN changes [7,10].

As plotted in Figure 2a, GPS PRN 28 was selected.

Zhong et al. [ 9] also reported that the ionospheric activity does not cause the long-term variations of the GPS sDCB.

However, sDCBs have relatively large RMS values for solar maximum from about 2000 to 2003, as shown in Figure 2b.

In addition, the filtered sDCB RMS values for GPS PRN 28 show very similar periodic patterns.

These cycles can be coincident with a period of a draconitic year of GPS satellites suggested by Zhong et al. [ 9].

Furthermore, there was a significant jump in changes in GPS PRN 28 sDCB RMS in 2015.

In early 2015, CODE changed a solar radiation pressure (SRP) model from the old empirical CODE orbit model (ECOM) to the new ECOM2 for GNSS orbit determination [11].

A change in the CODE SRP model can cause DCB variations.

For the relationship between sDCB RMS and rDCB RMS, we plotted the band-filtered rDCB RMS as the blue dots in Figure 2b.

There is some correlation between the two variables.

The correlation coefficient (R) between the two variables was about 0.58.

It indicates that there is a moderate correlation between sDCB RMS and rDCB RMS.


Choi et al. [ 8] suggested that the correlation between TEC and rDCB RMS shows the apparent latitudinal dependency with ionosphere variability.

It can be related to TEC distributions.

Zhang et al. [ 12] reported that the accuracy of rDCB estimation is dependent on the receiver’s location.

The rDCB estimation accuracy can be lower in low-latitude regions because TEC distributions at low latitudes are much more complicated than those at mid and high latitudes [13,14].

Strugarek et al. [ 15] reported that the GNSS signal is more affected during the periods of the high ionospheric activity, which causes problems with precise orbit determination of low Earth orbit satellites.

What we emphasize is that it can be difficult to specifically explain the latitudinal dependency of rDCB with changes in GPS sDCB only.

In general, the global ionospheric TEC is modeled by the spherical harmonic function.

To separate the satellite DCBs and rDCBs in ionospheric GPS-TEC estimation, we consider a constraint that the sum of all GPS satellite DCBs becomes zero.

Under this condition, variables such as TEC, satellite DCBs, and rDCBs have to be calculated simultaneously.

Each of the estimated variables can be influenced by each other.

As suggested by Zhong et al. [ 9], there was a clear correlation between sDCB and rDCB.

However, we suggest that variables are not estimated as an independent variable but are correlated.

Therefore, rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.


Iron deficiency is one of the most prevalent co-morbid conditions in chronic heart failure.

In the absence of any iron treatment, it is estimated that up to 50% of patients with heart failure have low levels of available iron.

1 The prevalence of iron deficiency is higher in the more advanced stages of heart failure (NYHA class III and IV), in females, and in patients with high levels of inflammatory markers (such as C-reactive protein) as well as increased levels of NT-proBNP, 2,3 but, even in patients at lower risk such as those with NYHA class I or II, the prevalence remains >30%.

1,2 These estimates include both absolute iron deficiency (in which iron stores are low) and functional iron deficiency (in which iron supply is inadequate to meet the demand for erythropoiesis and other cellular functions despite normal or abundant body iron stores).

The two states have different aetiologies.

Various pathological changes in chronic heart failure predispose patients to absolute iron deficiency, which is usually defined as ferritin <100 ng/mL. 4 -6 Poor nutrition is frequent, and patients with concomitant renal disease may be advised to follow a low-protein (and thus low-iron) diet.

7 Low intake of iron can be compounded by physiological and functional changes to the intestinal wall which may impair iron uptake from the gut, including the presence of mucosal oedema, and reduced gastrointestinal blood flow 8 (Table 1).

Patients with heart failure are also at risk of iron loss secondary to gastritis or ulceration caused by concomitant phar- macotherapy, and from proteinuria arising from chronic renal disease.

9,10 Lastly, administration of antiplatelet drugs or anticoag- ulation agents increases susceptibility to bleeding, with consequent blood loss.

11 Functional iron deficiency, in contrast, arises from the generalized inflammatory state which characterizes heart © 2015 The Authors.

European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. 249 failure.

Increased levels of proinflammatory cytokines such as interleukin-6 (IL-6) enhance production and release of hepcidin, an acute phase protein synthesized in the liver.

Hepcidin regulates the release of stored iron from enterocytes and hepatocytes by causing degradation of the iron transporter protein, ferroportin.

As a result, high levels of hepcidin can 'trap' iron in storage cells.

6,12,13 In a recent study published by Jankowska et al., the early stages of systolic heart failure were characterized by high levels of circulating hepcidin.

14 However, hepcidin levels declined as the severity of heart failure progressed, and as iron deficiency developed.

Moreover, low hepcidin was independently associated with unfavourable outcome.

14 The authors hypothesized that the pathophysiological mechanisms underlying iron deficiency in heart failure are markedly different from those in chronic inflammatory diseases, a possibility that requires further evidence to be con- firmed.

Functional iron deficiency is usually defined in heart failure as ferritin <300 ng/mL with transferrin saturation (TSAT) <20%.


While iron deficiency is usually identified in the clinic based on levels of ferritin and TSAT, due to their ease of use and wide availability, it should be noted that either inflammation or oxidative stress may artificially increase ferritin concentrations, regardless of actual iron status.

In contrast to other clinical settings, the ferritin threshold defining deficient iron storage is increased to 100 ng/mL in heart failure patients to take account of the chronic inflammatory state.

The recent FAIR-HF 15 and CONFIRM-HF 16 trials achieved beneficial results when ferritin and TSAT were used to define inclusion criteria and thresholds for maintenance treatment, and the European Society of Cardiology (ESC) Guidelines for heart failure 2012 included cut-off values for iron deficiency based on the FAIR-HF study.

17 Nevertheless, diagnosis based on ferritin and TSAT may be inadequate in advanced disease 18 or acute heart failure, 4,19 when ferritin becomes an unreliable marker.

Iron deficiency can therefore be underdiagnosed in these types of patients.

Measurement of soluble transferrin receptor, hepcidin, or reticulocyte haemoglobin (Hb) content may offer a more sensitive assessment of iron status and response to therapy, 4 and these techniques may become more widely adopted in the future.


Iron deficiency is closely associated with low Hb levels in patients with heart failure.

3,20 A large population-based study in Canada has suggested that anaemia of chronic disease predominates, followed by iron deficiency anaemia, 21 but the two types both primarily arise from insufficient iron available for haematopoiesis in the erythrocytes.


Conventionally, attention has focused on the effect of inadequate iron supply on erythropoiesis, and the resulting risk of anaemia.

Awareness is growing, however, of the many haematopoietic func- tions of iron.

Iron is an essential cofactor for haem and non-haem proteins involved in cellular activities such as oxygen storage (as a component of myoglobin), generation of cellular energy (as part of oxidative enzymes and mitochondrial respiratory chain proteins) in skeletal muscle and myocardiocytes, and the synthesis and degra- dation of lipids, carbohydrates, and nucleic acids.

22 In patients with heart failure, it has been shown that the myocardium has a reduced iron content, 23 and it is possible that myocardial iron depletion plays a role in deteriorating systolic function.


Several observational studies have assessed the asso- ciation between iron deficiency and outcomes in patients with heart failure (Table 2).

1 -3,21,24-27 Regardless of whether or not patients had anaemia, or the severity of heart failure, the presence of iron deficiency showed a significant association with mortality after adjustment for confounding factors in most studies.

1,2,24,25 Only in one analysis, a community-based study in the USA, was no correlation found, but in this analysis disease severity (measured with NYHA functional class and NT-proBNP levels) was not assessed.

3 Overall, the increase in mortality risk appears to be in the order of 40-50%.


Animal and human studies have suggested a strong causal rela- tionship between all levels of iron deficiency-even in the absence of anaemia-and the extent of physical activity.

29 Data from iron-deficient anaemic rats have shown a substantial improvement in walking ability within 15-18 h of iron administration despite unaltered Hb levels.

30,31 In heart failure, there is evidence that iron deficiency, and not anaemia, is an independent predictor of exercise intolerance in patients with heart failure.

In a prospec- tive study, peak oxygen consumption (VO 2 ) and the ventilatory response to exercise (VE-VCO 2 slope) were measured in 155 patients with stable heart failure (NYHA class I-IV) and mean LVEF of 26%.

26 Iron deficiency was defined as ferritin <100 ng/mL, or 100-300 ng/mL with TSAT <20%.

The results consistently indi- cated a significantly reduced exercise tolerance in patients with iron deficiency vs. patients with adequate iron, a difference that was maintained after adjustment for demographic and clinical variables.

26 These data confirmed findings from a previous prospec- tive study in 27 heart failure patients in whom iron deficiency was defined as TSAT <20%.


The negative influence of iron deficiency on exercise tolerance can be linked to the non-haematopoietic effects of iron, especially if one considers its role in mitochon- drial function in cells with high energy demand, such as cardiomy- ocytes and skeletal myocytes.

4,23 Mitochondria are the key sites of cellular iron processing, where synthesis of iron-sulfur (Fe/S) clusters and haem takes place, but are also the location for gen- eration of reactive oxygen species (ROS).

The activity of mito- chondrial Fe/S cluster proteins is reduced in failing hearts, an effect attributed to the lack of an Fe/S cluster centre.

33,34 On the other hand, clinical data and evidence from animal models with specific genetic defects have suggested that myocardial iron overload 35 or accumulation of iron in the mitochondria can induce or exacerbate cardiomyopathy, 34 presumably as a consequence of ROS-dependent mechanisms.

The question of whether iron accu- mulates in the mitochondria of failing hearts due to a shared aeti- ology, such as hypertension or myocardial infarction, has not been examined.


In a post-hoc analysis of a cohort of >500 chronic heart failure patients, iron deficiency was associated with lower quality of life than in those patients with normal iron levels, regardless of anaemia status.

In multivariate analysis, the effect on quality of life © 2015 The Authors.

European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

The pathophysiological impact of iron deficiency in heart failure patients with preserved ejection fraction (HFpEF) is not yet fully elucidated.

An observational study of 751 patients with heart failure which included a subpopulation of patients with HFpEF (defined as LVEF >50%) found that functional iron deficiency (TSAT <20%) occurred with a similar frequency in patients with or without HFpEF.

36 One well-designed study of 26 patients with HFpEF observed no significant association between iron deficiency (defined as ferritin <100 ng/mL or 100-299 ng/mL with TSAT <20%) and exercise capacity.

27 However, multivariate analysis of data from an epidemiological multicentre study of 1278 patients with heart failure showed an association between iron deficiency and health-related quality of life which was independent of whether LVEF was preserved or reduced.

28 Further studies with larger populations are needed to determine whether iron therapy can improve symptoms and cardiac function in HFpEF patients.


Oral iron therapy is relatively inexpensive and widely used, but the absorption of iron is low.

It also means that iron uptake can be inadequate to meet enhanced demand during treatment with an erythropoiesis-stimulating agent (ESA), limiting the haematopoietic response.

37 Oral iron is most frequently given in the form of ferrous (Fe[II]) salts, such as ferrous sulfate.

Oxidation of Fe(II) to Fe(III) can provoke oxidative damage at the mucosal boundary, resulting in local toxicity which leads to gastrointestinal side effects in up to 60% of patients.

38 These include complications such as constipation, dyspepsia, nausea, diarrhoea, and heartburn, which are particularly frequent with ferrous sulfate preparations and often lead to poor compliance.

Many constituents of food and some medications interact with ferrous salts, such that iron uptake is best when taken on an empty stomach, but gastrointestinal side effects are often improved by concomitant food, so there can be a difficult balance between maximizing uptake and minimizing gastrointestinal side effects.


Oxidative stress, with the potential for adverse consequences, has been observed in animal studies during treatment with fer- rous salts, especially at high doses.

Evidence suggests that this may include disruption of cell membranes, lipid peroxidation, DNA strand breakage, and immunological disturbances.

39 Moreover, fer- rous salts have been found to increase the severity of experimental colitis and affect the intestinal microbiomass due to redox reac- tions which lead to the production of ROS and mediate intesti- nal damage in rodents, 40 with evidence of increased prevalence of intestinal adenomas.


Long-term iron overload via dietary iron in rats induces necrosis in multiple organs, an effect attributed to cellular iron accumulation and consequent oxidative stress.

42 The effects of oxidative stress can be avoided by use of the oral prepara- tion iron[III]-polymaltose complex, which includes a polymaltose shell for stability so that iron is released in a controlled manner and non-transferrin-bound iron (NTBI) does not increase.

43 The structure of the iron[III]-polymaltose complex also minimizes the interactions with food or concomitant medications that can affect uptake of iron from ferrous salts.


Clinically, data from patients © 2015 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

with inflammatory bowel disease have demonstrated a significant increase in lipid peroxidation following ferrous sulfate treatment for 14 days compared with iron[III]-polymaltose complex.

44 As a consequence of the adverse events associated with oral iron therapy, it is considered unsuitable for patients who have defec- tive gastrointestinal absorption, those who cannot tolerate the required oral dose, or individuals who show poor compliance.

39 Oral iron is also unlikely to be adequate if a rapid effect is required, or if the rate of chronic iron loss exceeds the replenishment rate that is possible with oral administration.


Intravenous (i.v.) iron preparations bypass the absorption difficul- ties associated with oral iron, even in the presence of elevated hepcidin levels.

43,45,46 A series of i.v.

iron complexes has been devel- oped which enclose an iron-containing core within a carbohydrate shell, stabilizing the structure and controlling the release of iron.

43 The first complex, iron sucrose, was followed by low and high molecular weight iron dextran products and ferric gluconate.


More recently, ferric carboxymaltose, iron isomaltoside 1000, and feru- moxytol have become available.

These are more thermodynami- cally stable, with a low reduction potential, and permit administra- tion of a much higher dose of iron in a single administration without formation of large amounts of NTBI.

47 Among i.v. preparations, the less stable formulations, such as iron gluconate, are more prone to reduction, with greater risk of oxida- tive stress, so that the maximum single dose is smaller.


Experimen- tal evidence has shown significant differences between the effects of different i.v.

iron preparations.

Increased levels of markers for oxidative stress and inflammation have been observed in animals treated with sodium ferric gluconate, iron dextran, ferumoxytol, or iron isomaltoside 1000 compared with ferric carboxymaltose or iron sucrose, changes that were accompanied by hypotension and impaired liver and kidney function.

47 -50 However, it should be emphasized that most non-clinical in vivo studies have been con- ducted with iron doses that are significantly higher than those used in clinical practice.


Animal data showing that ferric carboxymaltose may have a favourable safety profile in terms of oxidative stress compared with iron dextran and ferric gluconate await confirma- tion.

Clinical data are limited, and are largely restricted to patients receiving haemodialysis.

Two recent studies which used standard doses of iron sucrose reported a transient (1-2 h) increase in NTBI and oxidative stress, accompanied by a short-lived increase in lipid peroxidation.

51,52 These results remain unconfirmed, and interpretation is complicated by the complex effects of chronic haemodialysis on oxidative stress, inflammation, and endothelial dysfunction.


It is challenging to compare accurately the risk of hypersensitivity reactions between different i.v.

iron preparations since events are very rare and reporting can be poor.

I.v.

iron complexes that contain dextran ligands can cause dextran-induced anaphylactic reactions.

These are rare, particularly when using low molecular weight dextran preparations, 53,54 but are serious events and have led to recommendations that i.v. iron dextran should be avoided.


Iron is essential for mounting an effective immune response, 56 but it is also biologically plausible that excess iron can impair normal neutrophil and T-cell functioning, with an associated risk of increased microbial growth.

57 The clinical evidence is largely based on observational studies on haemodialysis patients, which have yielded conflicting results, but taking a cautious approach the current Kidney Disease Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Anemia in Chronic Kidney Disease advises avoiding i.v.

iron therapy in patients with active systemic infections.

58 Conversely, withholding iron therapy with consequent iron deficiency may increase the risk of infections.

57 Randomized controlled trials are needed to evaluate all impor- tant safety endpoints, including infection.

Ongoing monitoring of newer i.v.

agents such as ferric carboxymaltose, ferumoxytol, and iron isomaltoside 1000 is essential to establish an extensive evi- dence base similar to that which already exists for earlier prepa- rations such as iron sucrose.

Additional clinical trials are being undertaken to help provide such data, in response to requests from regulatory authorities.


The recent ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012 recognize iron deficiency as a common co-morbidity in heart failure, independent of anaemia, which can contribute to muscle dysfunction and cause anaemia.

17 The guidelines recommend that assessment of all patients with suspected heart failure should include an evaluation of iron status to detect iron deficiency using ferritin and TSAT levels, but this is not yet regarded as standard practice.

59 A suggested algorithm for the diagnosis of iron deficiency in patients with heart failure is shown in Figure 1, based on typical thresholds for ferritin and TSAT in this setting.

15,17,60 Iron therapy should be considered in all patients with heart failure who have iron deficiency regardless of whether anaemia is present or not.

If ESA therapy is indicated for the treatment of anaemia-predominantly in patients with renal dysfunction-concomitant iron therapy may be necessary to meet the enhanced demand for iron during ESA-stimulated erythropoiesis.

5 The majority of heart failure patients receive oral iron therapy, with only ∼1 in 10 iron-treated patients being given i.v.

therapy.

61 Certain characteristics of patients with heart failure which can limit intestinal absorption of oral iron preparations can be partic- ularly pronounced and therefore i.v.

iron is the preferred option.


The median required dose of iron to achieve iron repletion in iron-deficient patients with heart failure is 1000 mg.

67 In patients with iron deficiency anaemia, the rate of iron bioavailability from a therapeutic dose of oral 100 mg ferrous sulfate, the most commonly used oral iron preparation, and iron[III]-polymaltose complex, is ∼10%.

68,69 Given that a typical dose of ferrous sulfate is 100-200 mg iron/day, in the 'best case' scenario, a patient who tolerates treatment of 200 mg/day might absorb only 20 mg of iron and thus require 50 days to correct the iron deficit.

In a 'worst case' scenario, given that patients with chronic heart failure may have as little as half the usual rate of active carrier-mediated intestinal absorption capacity, 62 a patient who can tolerate up to 100 mg iron/day might absorb only 5% (5 mg).

It would thus require 200 days to replenish iron stores.

In practice, correction of iron deficiency for heart failure patients will often take longer due to non-adherence or interruption due to gastrointestinal side effects, or missed doses due to the high pill burden.


Therefore, replen- ishment of iron stores with oral iron can be expected to require >6 months.

In contrast, one or two doses of i.v.

iron therapy can achieve rapid repletion (Table 3).

I.v.

administration, however, places additional demands on the treatment centre (Table 3).


Despite the fact that oral iron is still often the iron therapy of choice for many cardiologists, the evidence base related to oral iron therapy in heart failure is limited.

No randomized trials have compared oral iron vs. no iron therapy in the absence of ESA therapy.

In randomized trials of ESA therapy with oral iron vs. oral iron alone, mean iron parameters and Hb level, symptom severity, and exercise tolerance remained unaltered in the oral iron treatment groups 72 -75 (Table 4).


In the largest of these, 319 patients with symptomatic heat failure and Hb in the range © 2015 The Authors.

European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

9-12.5 g/dL were randomized to darbepoetin or placebo, both with daily oral iron therapy.

73 At the end of the 27-week study period, mean values for Hb were almost unchanged compared with baseline in the patients receiving only oral iron (11.6 g/dL vs. 11.3 g/dL at baseline), with little change in ferritin and TSAT levels.

Mean NYHA class was 2.54 compared with 2.67 at baseline, while maximum exercise duration decreased from 409 s to 363 s during the study.

73 The placebo-controlled IRONOUT study (NCT02188784) is currently underway to assess whether treatment with oral iron polysaccharide therapy can improve functional capacity in iron-deficient patients with heart failure.


Three single-arm trials have assessed the effect of i.v.

iron without ESA therapy on Hb level and iron indices in patients with heart failure, using iron sucrose or iron dextran preparations.

76 -78 The entry criteria for the three trials varied in terms of severity of heart failure, the presence or absence of anaemia and iron deficiency, and duration of follow-up (12-26 weeks), but in each case there was a significant increase in mean values for Hb, ferritin, and TSAT from baseline.

Upon treatment with i.v.

iron, various cardiac endpoints also improved significantly, including NYHA functional class, 76,78 LVEF, 77 and exercise tolerance.

76,78 These results are consistent .

with findings from single-blinded or double-blinded randomized trials of monotherapy with i.v.

iron in heart failure patients with iron deficiency, in which ferric carboxymaltose 15 or iron sucrose 32,79 were administered (Table 5).


Two randomized studies which used iron sucrose therapy demon- strated improved exercise capacity, cardiac function, symptom severity, and quality of life 32,79 compared with placebo.


Okonko et al. showed that the benefits of i.v. iron were seen in patients with or without anaemia at baseline, but were greater in anaemic individuals.


In a second trial, 40 patients with anaemia, iron defi- ciency, and LVEF <35% were randomized to placebo or i.v.

iron sucrose therapy for 5 weeks in a 6-month, double-blind study 79 then followed to 5 years.


At 6 months, the number of hospital- izations was significantly lower in the patients who had received i.v.

iron therapy compared with the placebo-treated group {0 events vs. 5 events (P < 0.01); relative risk 2.33 for hospitalization with placebo vs. i.v.

iron [95% confidence interval (CI) 1.59-3.42]}. 79


Hospitalization remained significantly less frequent with i.v.

iron vs. placebo at 1 year (10% vs. 50%) and 5 years (20% vs. 85%) (both P < 0.01) and, notably, mortality was significantly lower at 5 years (20% vs. 55%, P < 0.05).


These findings require confirmation.


FAIR-HF was a randomized, double-blind, placebo-controlled, 24-week multicentre study of 459 patients with heart failure (NYHA class II-III) and iron deficiency, with or without anaemia.

15 Patients were enrolled if their Hb level was between 9.5 and © 2015 The Authors.

European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

non-dialysis-dependent patients.


90 Levels of Hb, ferritin, and TSAT were all significantly increased by i.v.

iron vs. oral iron, and there was a significant reduction in requirement in ESA dose in dialysis-dependent patients treated with i.v.

iron.

The scale of the mean between-group differences was clinically relevant: 0.90 g/dL for Hb, 243 ng/mL for ferritin, and 10.2% for TSAT.


This is consis- tent with recent randomized trials using i.v.

ferumoxytol in patients with chronic kidney disease with or without a requirement for haemodialysis 91,92 and using i.v.

ferric carboxymaltose in patients with non-dialysis-dependent chronic kidney disease, 93,94 each of which showed that the haematopoietic response was significantly greater with i.v.

iron vs. oral iron therapy.


In post-partum iron deficiency anaemia, numerous random- ized trials have compared the efficacy and safety of i.v.

vs. oral iron.

95 These have consistently shown a more rapid improvement in iron stores and Hb levels with i.v.

preparations, accompanied by improved fatigue and quality of life vs. oral iron therapy, with improved compliance.

Using non-dextran i.v. iron preparations, safety was good, with fewer gastrointestinal adverse events than oral iron.


Similarly, in chemotherapy-induced anaemia, a recent meta-analysis of 11 randomized trials of i.v. vs. oral iron ther- apy concluded that i.v. iron was associated with an improved haematopoietic response with no safety concerns, and that the requirement for blood transfusion was lower than with oral iron.


Lastly, several randomized trials testing the effect of i.v. vs. oral iron therapy in patients with iron deficiency anaemia associated with inflammatory bowel disease have shown a faster and more pro- longed haematopoietic response with i.v.

therapy, leading to recom- mendations from the 'Guidelines on the diagnosis and management of iron deficiency and anemia in inflammatory bowel diseases' that i.v. should be the preferred route for administration.

97 One recent large randomized trial of 338 patients with inflammatory bowel .

i.v.

iron isomaltoside 1000 in terms of the pri- mary efficacy endpoint of change in Hb from baseline to week 8 (P = 0.09), but patients with known intolerance to oral iron were excluded and i.v.

iron dosing may have been inadequate; therefore; further trial data are awaited.


Across a range of clinical settings, randomized trials have shown that oral iron therapy does not replenish iron stores, including non-dialysis-dependent chronic kidney disease, 91 -93 inflammatory bowel disease, 98,99 heavy uterine bleeding, 100 and post-partum anaemia.

101 -103


Iron deficiency is widespread in patients with heart failure.

Cur- rently, however, it is not universally monitored or managed despite convincing evidence that iron deficiency is a risk factor for poor outcome independent of anaemia status.

Iron is poorly absorbed from oral preparations, compounded by chronic inflammation and other contributing factors in patients with heart failure, which delays or prohibits iron replenishment.

Evidence for a clinical ben- efit using oral iron preparations in heart failure is lacking.

Clin- ical studies have instead focused on the use of i.v.

iron, which bypasses the problem of gastrointestinal absorption and poor com- pliance.

Early studies using iron sucrose consistently showed that correction of iron deficiency improved exercise capacity, cardiac function, symptom severity, and quality of life.

32,76,77,79 These stud- ies were recently confirmed in the large 6-month FAIR-HF study, where functional capacity, symptoms, and quality of life rapidly increased in patients with heart failure of NYHA class II-III and impaired LVEF 15 in comparison with placebo treatment.

Longer term data were provided by the CONFIRM-HF trial, in which symptomatic, iron-deficient patients with heart failure showed a sustained improvement in functional capacity, symptoms, and qual- ity of life, and reduced risk of hospitalization due to worsening heart failure over 1 year of i.v.

iron therapy.

16 The ESC Guide- lines point out that ferric carboxymaltose may be considered for treatment of these patients, although the effect in patients with pre- served LVEF remains unknown.

17 It should also be borne in mind that assessment of the hard endpoint of mortality is not feasible within the population sizes possible in controlled studies, and that a full assessment of safety would require more extended follow-up.


The structures of new i.v. preparations permit far larger doses of iron to be administered safely in a single visit.

Routine diagnosis and management of iron deficiency in patients with symptomatic heart failure, regardless of anaemia status, and prompt interven- tion using i.v.

iron therapy should now be considered, pending the outcome of larger ongoing randomized controlled trials and longer term safety data.


Anaemia is increasingly recognised as having an independent impact upon patient outcomes in cardiac disease.

The role of novel iron therapies to treat anaemia is increasing.

This systematic review and meta-analysis assesses the efficacy and safety of iron therapies for the treatment of adults with anaemia.

Electronic databases and search engines were searched as per Cochrane methodology.

Randomised controlled trials (RCTs) of iron versus inactive control or placebo, as well as alternative formulations, doses and routes in anaemic adults without chronic kidney disease or in the peri-partum period were eligible.

The primary outcome of interest was mortality at one year.

Secondary outcomes were blood transfusion, haemoglobin levels, quality of life, serious adverse events and length of hospital stay.


64 RCTs (including five studies of heart failure patients) including 9004 participants were included.

None of the studies were at a low risk of bias.

There were no statistically significant differences in mortality between iron and inactive control.

Both oral and parenteral iron significantly reduced the proportion of patients requiring blood transfusion compared to inactive control (risk ratio (RR) 0.66 (95% confidence interval (CI) 0.48 to 0.90) and RR 0.84 (95% CI 0.73 – 0.97) respectively).

Haemoglobin was increased more by both oral and parenteral iron compared to inactive control (mean difference (MD) 0.91 g/dL (95% CI 0.48 to 1.35) and MD 1.04 (95% CI 0.52 to 1.57) respectively), and parenteral iron demonstrated a greater increase when compared to oral iron (MD 0.53 g/dL (95% CI 0.31 to 0.75)).

In all comparisons there were no differences in the results comparing patients with and without heart failure.

Both oral and parenteral iron are shown to decrease the proportion of people who require blood transfusion and increase haemoglobin levels, without any benefit in mortality.

Further trials at a low risk of bias, powered to measure clinically significant endpoints are still required.

This paper updates the findings of a Cochrane review first published December 2014 in The Cochrane Database of Systematic Reviews (CDSR) Issue 12 (http://www.thecochranelibrary.com).


This article refers to ‘Effects of intravenous iron therapy in iron-deficient patients with systolic heart failure: a meta-analysis of randomized controlled trials’, by E.A. Jankowska et al., published in this issue on pages 786–795, and ‘Systematic review and meta-analysis of iron therapy in anaemic adults without chronic kidney disease: updated and abridged Cochrane review’, by B. Clevenger et al., published in this issue on pages 774–785.


Over the last 25 years, treatments for chronic heart failure (HF) with reduced systolic function (HF-REF) have emerged that significantly reduce both mortality and morbidity.

However, other phenotypes of the HF syndrome, particularly HF with preserved systolic function (HF-PEF), still lack effective therapies.

In addition there remains a substantial unmet need for further reductions in modifiable mortality and the avoidance of frequent and costly hospitalizations in HF-REF.

As HF is mainly a disease of the elderly, much attention is now being paid to management of the frequent co-morbidities which are so prevalent in HF patients.


Anaemia and iron deficiency (ID) have emerged as important co-morbidities as both are prevalent in chronic HF and their occurrence is associated with an increase in both morbidity and mortality [1], [2].


There is a growing body of evidence that treating ID in HF-REF patients, irrespective of whether they are anaemic or not, improves outcomes.

The majority of studies have used i.v.

iron preparations.

The use of oral iron preparations has been limited for a number of reasons: their high side effect profile, the time taken to correct ID, poor intestinal absorption which can be due to mucosal oedema, and emerging evidence that HF patients have high circulating concentrations of hepcidin which inhibits the release of stored iron for use in haematopoiesis [3].


Initially small, non-randomized open label studies and randomized controlled trials (RCTs) reported an improvement in surrogate endpoints such as symptoms, quality of life, LVEF, BNP concentration, effort capacity, and peak VO2 [4], [5], [6].


These were followed by two larger RCTs of i.v.

iron compared with usual care.

The FAIR-HF trial recruited 459 patients in NYHA class II/III, with an LVEF ≤40% (NYHA II) or ≤45% (NYHA III), haemoglobin concentrations in the range of 9.5–13.5 g/dL, with evidence of ID demonstrated by a serum ferritin <100 ng/mL, or <300 ng/mL if transferrin saturation (TSAT) was <20%.

Intravenous iron was associated with significant improvements in NYHA class, effort capacity, and quality of life [7].

It was followed by the CONFIRM-HF trial which demonstrated in 304 patients (with similar inclusion criteria), over a longer follow-up period of 1 year, sustained improvements in effort capacity and quality of life [8].

Neither trial was powered to demonstrate a reduction in harder, more traditional HF outcome measures, such as mortality or HF hospitalizations.


With the emerging evidence base, meta-analyses are being performed to try to determine whether the use of i.v iron may be associated with reductions in mortality as well as morbidity.

Two such aggregate data meta-analyses (random effects model) have now been published in this journal.

The first by Jankowska et al. focused on i.v iron therapy in patients with systolic HF and ID [9].

They identified five trials which fulfilled the inclusion criteria (509 patients received i.v iron therapy in comparison with 342 controls).

Intravenous iron therapy reduced the risk of a combined endpoint of all-cause death or cardiovascular hospitalization [odds ratio (OR) 0.44, 95% confidence interval (CI) 0.30–0.64, P < 0.0001] and a combined endpoint of cardiovascular death or hospitalization for worsening HF (OR 0.39, 95% CI 0.24–0.63, P = 0.0001).

It resulted in a reduction in NYHA class, an increase in 6-min walking test distance, and an improvement in quality of life [Kansas City Cardiomyopathy Questionnaire (KCCQ) score: +5.5 points, 95% CI 2.8–8.3, P < 0.0001; European Quality of Life–5 Dimensions (EQ-5D) score: +4.1 points, 95% CI 0.8–7.3, P = 0.01; Minnesota Living With Heart Failure Questionnaire (MLHFQ) score: 19 points, 95% CI −23 to −16, P < 0.0001; Patient Global Assessment (PGA): +0.70 points, 95% CI 0.31–1.09, P = 0.004].


The second, by Clevenger et al., differs in a number of aspects [10].

It looked at a wider group of patients with anaemia, and the iron therapy could be either i.v. oral.

They included RCTs of iron vs. inactive control or placebo, in anaemic adults without chronic kidney disease or in the peripartum period.

As such, patients were included with inflammatory bowel disease, gynaecological problems, those undergoing chemotherapy, as well as HF patients.

The primary outcome was mortality at 1 year.


They report results on 9004 patients in 64 RCTs (including five studies of HF patients).

None of the studies included was at a low risk of bias.

No statistically significant differences were seen in mortality between iron and inactive control.


Both oral and parenteral iron significantly reduced the proportion of patients requiring blood transfusion compared with inactive control [risk ratio (RR) 0.66, 95% CI 0.48–0.90, and RR 0.84, 95% CI 0.73–0.97, respectively].

Haemoglobin was increased more by both oral and parenteral iron compared with inactive control [mean difference (MD) 0.91 g/dL, 95% CI 0.48–1.35, and MD 1.04, 95% CI 0.52–1.57, respectively], and parenteral iron demonstrated a greater increase when compared with oral iron (MD 0.53 g/dL, 95% CI 0.31–0.75).

In all comparisons, there were no differences between patients with and without HF.


These two meta-analyses add to the growing body of evidence which shows that treating anaemia and ID is beneficial for patients.

The first, in HF-REF patients with ID, suggests that harder composite endpoints are reduced by i.v.

iron therapy.

Mortality alone was not reduced.

In the second study, where mortality was the focus, it was also not reduced by iron therapy.

This is perhaps unsurprising due to the heterogeneous inclusion criteria and the differing 1-year mortality rates of the myriad conditions included which ranged from menorrhagia to cancer.


So where does this growing body of evidence take us in terms of the management of ID in patients with HF-REF? In the European Society of Cardiology (ESC) guidelines of 2012 we are advised to investigate the cause of anaemia in HF patients and that the treatment of ID in HF-REF patients may be considered.

The accumulating evidence is pointing to a stronger recommendation in the future.

However, we still await large, adequately powered, studies to determine the effects of iron on mortality and hospitalizations in HF-REF and to determine the nature of the iron supplementation that should be given as well as its long-term safety.


Appelt and Kronfeld had studied the genera- tion of referring expressions, focussing on a range of difficult issues arising from the fact that referring expressions are an integral part of a larger communica- tive act.

The resulting algorithms generated expressions which took discourse context into account (e.g. Appelt and Kronfeld, 1987; Kronfeld, 1989) and of- ten contained more information than was necessary to identify the referent, for example in order to make it easier for the hearer to carry out the process of identification, or in order to meet additional communicative goals (Appelt, 1985).

Although their work offered important insights, its success as a model of human reference was essentially unproven, and this started increasingly to be seen when computational linguists became more data oriented.

For this reason, Dale and Reiter decided to re-focus, concentrating on what they saw as the core of the problem, which is to identify a referent, focussing on simple situations, where nothing matters except the properties of the referent and of the other objects in the domain (the distractors).

Other aspects of the utterance context were temporarily disregarded, not because they were deemed unimportant, but because these researchers thought it wise to focus on simple things first.

Even though this re-focussing on a narrower view of reference was not universal - some continued to investigate the effects of linguistic context (e.g. Passonneau, 1995; Jordan, 2002; Stone et al., 2003) and the interaction with other commu- nicative goals (e.g. Jordan, 2002; Stone et al., 2003) - it has exerted a strong influence on the direction of computational gre.


Let us spell out Dale and Reiter's assumptions in more detail.

The language- generating program takes as its input a knowledge base (kb) whose content is mutually known by speaker and hearer.

The kb ascribes properties to ob- jects, formulated as a combination of an attribute and a value.

If the domain objects are pets, for example, then one attribute might be type, with values such as dog and poodle.

The first aim of gre is to find properties that iden- tify the intended referent uniquely, using a semantic form like ( type: poodle , colour: brown ).

Computing such a semantic form is called Content Deter- mination.

The second aim is to express the semantic form in words, for example the poodle which is brown.


Dale and Reiter examined a number of algorithms, all of which were based on interpretations of the Gricean Maxims, which revolve around brevity, relevance and truth (Grice, 1975).

Roughly speaking, these algorithms sought to minimise the redundancy in a description.

In the most extreme case (e.g. Dale, 1989), the Gricean maxims had been interpreted as dictating the choice of the smallest set of properties of a referent that will uniquely identify it.

Dale and Reiter contrasted this with a more relaxed interpretation of the maxims, which gave rise to what has come to be known as the Incremental Algorithm (ia).

They argued that, in the situations envisaged, the ia produces referring expressions that resemble human-generated referring expressions better than its competi- tors.

Following Belz and Gatt 2008, we shall call this the criterion of human- likeness.

Dale and Reiter also argued that the ia is computationally tractable, and that this constitutes another argument in favour of it.

The two arguments can be seen as related if one assumes that neither people nor machines can solve computationally intractable problems in real time.


Dale and Reiter's empirical position was based on a reading of the psycholinguistic literature (particularly as summarised in Levelt, 1989).

Yet - consistent with existing practice in natural language generation at the time - their paper did not include an empirical test.

Later work (Passon- neau, 1995; Jordan and Walker, 2000) has tested some of their ideas, as we shall see, but this work has tended to concentrate on different (in fact, more complex) referential situations than the ones on which Dale and Reiter focussed.

We aim to put Dale and Reiter's original ideas to the test.

Like many other studies, our investigation will focus on Content Determination (unlike (Stone et al., 2003; Krahmer and Theune, 2002)), disregarding words and syntactic constructions.

Like Dale and Reiter, we shall focus on the task of identifying the referent, dis- regarding the well-documented fact that referring expressions can serve other communicative purposes (e.g. Jordan, 2002; Stone et al., 2003), and we shall focus on "one-shot" referring expressions, as produced in a null context where words outside the referring expression do not play a role.


In one respect we have been less conservative.

For although Dale and Reiter focussed exclusively on singular descriptions, many references to sets can be generated by variations of the classic gre algorithms (e.g. van Deemter, 2002; Gardent, 2002; Horacek, 2004; Gatt and van Deemter, 2007).

This is some- thing we considered worth testing as part of our general plan.

In order not to contaminate the discussion of Dale and Reiter's claims, we only discuss plural references of the kind that can be generated by a relatively simple extension of the ia; moreover, we report results on singulars and plurals separately.

For generality, we have studied referring expressions in two different domain types, one of which involves references to furniture (the furniture sub-corpus) and the other to photographs of faces of people (the people sub-corpus).


The plan for the experiment which is the main focus of this paper was first outlined in van Deemter et al. ( 2006).

The first tentative results were reported in Gatt et al. ( 2007) concerning the furniture sub-corpus, and in van der Sluis et al. ( 2007) concerning the people sub-corpus.


The tuna corpus and evaluation method have influenced the field considerably, in particular after they were chosen as the basis on which gre algorithms were evaluated in the First NLG Shared Task and Evaluation Challenge (stec) on Attribute Selection for Referring Expressions Generation (asgre), in the Spring and Summer of 2007 5 . 22 Different algorithms were submitted to this stec for comparative evaluation, coming from 13 different research teams (Belz and Gatt, 2007).

tuna also featured in a subset of the tasks organised for the second stec in this area 6 , where the number of submitted systems was even greater (Gatt et al., 2008a).

As of October 2008, the annotated corpus is available from the Evaluations and Language resources Distribution Agency (elda) 7 .


The present paper offers a definitive statement of the aims and set-up of the tuna experiment, the structure of the annotated corpus, and our analyses of the corpus (based on a larger number of subjects, in a greater number of conditions, and validated against a second corpus), superseding our earlier papers on the topic.

Consistent with our aim of testing Dale and Reiter's original hypotheses, this empirical analysis is combined with a discussion of the computational com- plexity of the Incremental Algorithm and its main competitors, and concludes with a discussion of the advantages and disadvantages of incrementality.


In order to test Dale and Reiter's claims, we focus our investigation on the algorithms that were most prominently discussed in their paper.

We introduce the algorithms briefly before discussing them in more detail.


• Full Brevity (fb).

Conceptually this is the simplest approach, discussed in a number of early contributions (e.g. Appelt, 1985), and formalised by Dale (1989).

fb minimises the number of properties in the description.

Technically, fb is not one algorithm but a class, since minimality can be achieved by many different algorithms.

fb is computationally intractable, because in the worst case, the time it takes to find a minimal description grows exponentially with the number of properties that the program can choose between (Reiter, 1990).


• Greedy Algorithm (gr).

This faster algorithm was introduced as an ap- proximation to fb by Dale (1989).

Rather than searching exhaustively, it selects properties one by one, always choosing the property that is true of the intended referent and excludes the greatest number of distractors.

gr does not always produce a minimal description, because a property that removes the maximum number of distractors at the time of its inclusion might not remove the maximum number of objects in combination with properties that will later be added.


A specially constructed example will clarify how these algo- rithms can lead to different outcomes.

Imagine a small domain of objects {a, b, c, d, e, f, g}. Now consider the following five attributes, each of which has two values, denoted as val 1 and val 2 .

We choose values which are each other's complement.

Since complementary values do not overlap, this means that the choice between different values (of a given attribute) is always obvious once the referent is given.

Thus, here and elsewhere in this article, we will focus on the question of attribute selection, on which most research in this area has concentrated.


A different focus would have been possible, for example because one value (e.g., mammal) may be more general than another value (e.g., dog) of the same attribute.

( In section 4.2.2 we shall encounter this phenomenon.) The ia assumes that the different values of an attribute are always roughly equally preferred, with the choice between them depending on matters such as their discriminatory value (i.e., not on a fixed po).

This assumption can be questioned - If an elephant is pink its colour is more worth mentioning than if it is grey - and this can motivate a different ia, whose po defines an ordering on properties (i.e., combinations of an attribute and a value) rather than on attributes.


The choice of the best value of an attribute has, to the best of our knowledge, never been empirically investigated.

See Dale and Reiter (1995) (the FindBestValue function) for an algorithm and van Deemter (2002) for a logical analysis focussing on problems that arise when the values of an attribute overlap.

See also section 8.


Suppose the target referent is the dog e. fb will combine att2: val1 (black, which is true of the objects {a, b, c, e} only) with att4: val1 (outdoor, which is true of {d, e, f, g}), because these two properties jointly single out e while all other descriptions that manage the same feat happen to contain three or more properties.


The output thus consists of the properties black and outdoor.

Notice that realising this content into a description would not be straightforward, since neither property maps naturally to a head noun (it is usually the type of an en- tity which has this role).

This description might be realised as, for example, the black animal that lives outdoors, inserting the category or type of the referent (i.e., animal) artificially.


While gr often produces the same description as fb, this is not the case in the present example.

gr will start by selecting the property att1: val1 (poo- dle, corresponding to the set {c, e, f }), because it is the property that excludes most distractors.

Even though only two distractors are left, namely c and f , no single property manages to remove both of these at the same time.

As the next step, gr will select either att2: val1 (black, removing f ) or att3: val1 (bastard, removing c), but in each case a third property is required to remove the last remaining distractor.

gr does not generate the smallest possible de- scription, but something like the black poodle that lives outdoors instead.


What description is generated by ia? If the attributes are attempted in the order in which they were listed, so att 1 is attempted first, followed by att 2 , then a version of gr is mimicked precisely. But if att 2 is attempted first, followed by att 4 , then the result is the same as that of fb.

In other cases, much lengthier descriptions can result, for example when att 5 is attempted first (narrowing down the set of possible referents to {a, b, c, d, e, f }), and then att 2 (narrowing down the set of possible referents to {a, b, c, e}), followed by att 1 (resulting in {c, e}) and finally att 4 (resulting in the set {e} which contains only the target referent).

The resulting description might be realised as the black poodle with a tail, which sleeps outdoors.


This artificial example shows how dramatically the outcome of the ia can depend on po.

These differences make it difficult to test the hypothesis that ia's descriptions resemble human-generated descriptions.

Which of all the possible ias, after all, is the hypothesis about? It is possible that in most actually occurring situations, different pos lead to very similar descriptions, or ones that are similar in terms of their human-likeness.

We will show that this is not the case, and that there are important differences between the different versions of ia.

This outcome will raise the question how "good" pos might be selected.


The type attribute.

Types are usually realised as nouns.

poodle, for ex- ample, is a type.

Because a referring expression normally requires a noun (we tend to say the brown dog even when brown would identify the referent), Dale and Reiter gave types special treatment.


If type is not selected by the algo- rithm (e.g., because all objects are of the same type), ia adds the property to the description at the end of the search process.

Suppose, for example, the referent is a dog.

Now if the ia starts by selecting the property brown, and if this property happens to identify the referent uniquely, then the property type: dog is added, generating the brown dog.

This makes sure that every description contains one property realisable as a noun.


This special treatment of nouns - foreshadowed in Appelt (1985) - can be seen as independent of the search strategy.

In fact, we believe that the same considerations that make this a good move in combination with ia make it a good move in combination with other algorithms.

In our comparison between algorithms, therefore, we have levelled the playing field by making sure that fb and gr apply the same idea.


Each of the algorithms considered in this paper will therefore add a suitable value of the type attribute at the end of the algorithm.

This did not signifi- cantly change the outcome of any of the algorithms since, in the data we use for our evaluation, type was never either necessary or sufficient to identify a target referent, alone or in combination with other properties (see Section 4.2.3).


We decided to examine certain kinds of expressions that refer to sets as well.

We did this by applying a slightly generalised version of ia to these cases, simplifying a proposal in van Deemter (2002).

The original ia stopped adding properties to a description when they singled out the set whose only element is the referent.

The new ia does the same, stopping when these properties single out a set of referents.

The same idea extends naturally to fb and gr.

This tech- nique allows us, for example, to use the description ( att1: val1 , att2: val2 ) to refer to the set {c, e}. Note that this approach does not work for collective references (e.g. the parallel lines in this picture; cf.

Stone, 2000), which require drastic departures from the algorithms discussed by Dale and Reiter.


Another case which this algorithm will not handle is that requiring plural descriptions involving the union of two different sets, such as the dogs and the cats (van Deemter, 2002; Horacek, 2004; Gatt and van Deemter, 2007).

These cases too will be omitted from the present study.


Empirical work relevant to gre falls into two main classes.

On the one hand, psycholinguistic studies on reference span at least four decades, since the foun- dational work of Krauss and Weinheimer (1964).

On the other hand, partly as a result of increasing interest in comparative evaluation in Natural Language Generation, a number of studies have compared the ia and some other algo- rithms against corpus data.

We give an overview of the main findings from both types of research.


As we have seen, the starting point for contemporary gre research was the notion of Brevity.

Brevity was implicit in an early paper by Olson (1970), who argued that reference has a primarily contrastive function, so that the content of an identifying description would be expected to be determined by the distractors from which the referent is distinguished.


A substantial body of evidence now shows that brevity is not the only factor motivating speaker's choices of proper- ties in reference.

The phenomenon of overspecification, whereby speakers select properties which have little or no contrastive value, was observed in early de- velopmental studies (Ford and Olson, 1975; Whitehurst, 1976; Whitehurst and Sonnenschein, 1978); later studies on adult speakers confirmed the tendency.


Pechmann (1984) showed that adult speakers begin to articulate a reference be- fore their scanning of a visual domain is complete, and that perceptually salient attributes, such as colour, were apt to be selected before other properties, such as size, which required more cognitive effort because their value can be determined only by comparison to other objects.


Later work showed that type and colour were always used in referring expressions in visual domains, even when they had no contrastive value (Schriefers and Pechmann, 1988; Pechmann, 1989), a result not replicated for size.

According to these authors, type has privileged status not only because of syntactic constraints, but because speakers process a referent as a conceptual gestalt, central to which is the referent's object class; similarly, they argued that colour forms part of the speaker's mental representation of an object.


Similar results have been reported by Man- gold and Pobel (1988) and Eikmeyer and Ahlsèn (1996).

A slightly different interpretation is given by Belke and Meyer (2002), who interpret the finding in terms of an attribute's codability, that is, the ease with which that attribute can be included in a mental representation of an object.

Thus, relative attributes such as size are argued to have low codability.


Evidence for overspecification has also been found in connection with locative (Arts, 2004) and relational de- scriptions (Engelhardt et al., 2006).


The demonstration that speakers overspecify has obvious relevance to a study comparing the ia to its predecessors, since one of the consequences of a po is the potential for referential overspecification, with an increased likeli- hood that attributes which are given high priority (say, colour), will be used even though they are not required for a distinguishing description.


One of the first studies to systematically compare the ia to other algorithms fo- cussed on the coconut corpus, a collection of task-oriented dialogues in which interlocutors had to resolve a joint task (buying furniture on a fixed budget) (Jordan, 2000a).

9 Jordan's study compared the ia to two models.

Jordan's Intentional Influences (ii) model (Jordan, 2000a, 2002) views content determi- nation as heavily influenced by communicative intentions over and above the identification intention.

Thus, the intention to signal agreement with a pro- posal (in this case, to buy some item of furniture), may motivate the speaker to repeat a description produced by their interlocutor, for example.

The authors also included a computational implementation of the Conceptual Pacts model proposed by Brennan and Clark (1996), in which a speakers' choice of content for a referring expression is in part based on a tacit 'agreement' with the inter- locutor to refer to objects in a specific way.

The ia was outperformed by both models.

However, the comparison leaves open the question as to whether the ia is an adequate model of referent identification, since the models to which it was compared explicitly go beyond this.


Still within a dialogue context, Gupta and Stent (2005) carried out an evalu- ation on the coconut and the maptask (Anderson et al., 1991) corpora.

They compared the ia and a version of the Greedy algorithm by Siddharthan and Copestake (2004) against a baseline procedure that included the type of an ob- ject, and randomly added further properties until a referent was distinguished.

Additionally, each algorithm was augmented with dialogue-oriented heuristics, and coupled with procedures for the realisation of modifiers.

Thus, the eval- uation used an evaluation metric which combined (a) the degree of agreement between an algorithm's attribute selections and a human's, and (b) the extent to which the automatic realisation of attributes was syntactically a good match to the human realisation.


While these studies offer important insights, they do not directly address the questions outlined in the Introduction to our paper.

In the MapTask cor- pus, for example, most referents are named entities with no true distractors, which can explain why the baseline algorithm outperformed both ia and the Greedy algorithm on this data in Gupta and Stent's study.

In the coconut corpus, these two algorithms outperformed the baseline, but the original ia was outperformed by variants that incorporated dialogue-oriented heuristics.

This is exactly as one would predict, since identification is often not the only refer- ential goal of interlocutors, particularly in the coconut corpus, where other factors have been shown to be paramount (Jordan, 2000a; Jordan and Walker, 2005).

The evaluation metric used by Gupta and Stent incorporated syntactic factors, going beyond the purely semantic task-definition that the IA sought to address.

This, of course, is only a limitation from the viewpoint of a study like the present one, which focusses on Content Determination.


A study by Viethen and Dale (2006) offers a more straightforward compar- ison of the ia and the Greedy Algorithm.

Viethen and Dale stuck to the iden- tification criterion as the sole communicative intention.

They tested against a small corpus of 118 descriptions, obtained by asking experimental participants to refer to drawers in a filing cabinet, which differed on four dimensions, namely colour, row, column and whether or not a drawer was in a corner.

The primary evaluation metric used was recall, defined as the proportion of descrip- tions in the corpus which an algorithm reproduced perfectly.


The comparison of ia and gr revealed a recall rate of 79.6% for the latter, compared to a 95.1% for the ia (with both figures excluding relational descriptions).

Moreover, the corpus contained a limited number (29) of overspecified descriptions, of which the ia reproduced all but five.


Although these results seem favourable for the ia, they only tell us which descriptions are generated by one or more of all (4! =) 24 possible pos of the attributes.

This is because Viethen and Dale com- bined results from all 24 versions of the algorithm, a legitimate move, but one that obscures the extent to which a given version of the ia, with a particular po, contributes to the overall rate.