docs: state the fitting settings and the one-dataset scope

README.md

@@ -38,23 +38,32 @@ This dataset (`SynthStats/synthstats-demonstrations`, released under the MIT lic
 
 Each scored program is fitted to the 80 visible observations, then assessed by summing its log predictive densities at the other 20. Higher scores are better, but can be compared only within the same task. The comparisons below use a floor model, which predicts the same distribution at every input, and a reference program that uses the environment's data-generating form.
 
-In the dugong task, Qwen3.6-35B-A3B wrote eight PyMC programs (PyMC is a Python library for probability models). Three scored above the reference program's -4.6028: single attempt 2 (-3.1969), single attempt 4 (-3.2267) and loop proposal 4 (-4.5121). Single attempt 2 came from one call without feedback and had the highest score; the floor model scored -14.0898.
+In the dugong task, the prompted language model Qwen3.6-35B-A3B wrote eight PyMC programs (PyMC is a Python library for probability models). On the 20 held-out observations of this one simulated dataset, three scored above the reference program's -4.6028: single attempt 2 (-3.1969), single attempt 4 (-3.2267) and loop proposal 4 (-4.5121). The floor model scored -14.0898.
 
 For disease spread, none of its programs reached the reference (-45.6085), whereas all six of those that scored beat the floor model (-262.1291).
 
 In each task the language model received a description in English together with 80 observations of one simulated dataset, the other 20 being held out for scoring, and the prompt also contained a table of the columns together with the summary statistics of the 80 visible rows.
 
-As for the eight programs that it wrote per task, four are single attempts, while the other four come from a loop of two rounds, between which the language model, called again as a critic, reads the first round's fitted programs and their predictions, its comments going into the prompt for the last two. In both tasks its best loop proposal scored below its best single attempt.
+As for the eight programs that it wrote per task, four are single attempts, while the other four come from a loop of two rounds, between which the language model, called again as a critic, reads the first round's fitted programs and their predictions, its comments going into the prompt for the last two.
 
 For dugong growth, the reference program can be thought of as the environment's growth curve written in PyMC: three parameters with the environment's own priors, plus a fixed noise scale.
 
-So each task has ten programs, numbered 01 to 10: 01 is the reference, 02 is the floor model, 03 to 06 are single attempts 1 to 4, and 07 to 10 are loop proposals 1 to 4 (with 1 and 2 in round 1, and 3 and 4 in round 2).
+With the reference and floor model included, each task has ten programs, numbered 01 to 10. The reference is 01 and the floor model 02, followed at 03 to 06 by single attempts 1 to 4, then at 07 to 10 by loop proposals 1 to 4 (proposals 1 and 2 come from round 1, proposals 3 and 4 from round 2).
 
-A program that fails one of the checks gets no score. For disease spread, two programs (single attempt 4 and loop proposal 2) failed at model build, so they have no score and no predictions: they appear in the program table `programs` and in the program summary `disease_spread_program_summary`, and are absent from the prediction and curve tables.
+For disease spread, the two programs whose outcome in `programs` and `disease_spread_program_summary` is failure at model build are single attempt 4 and loop proposal 2. The prediction and curve tables have rows for programs that pass all [the checks](https://synthstats.org/one-program/#checks-before-fitting) and receive a score.
 
-The others are fitted to the 80 visible observations, with 200 tuning iterations followed by 200 retained posterior draws in one chain, each of which is then used 20 times to simulate a new observation, giving 4,000 predictions at every input (with PyMC 5.21.1, PyTensor 2.28.3 and ArviZ 0.23.4).
+## Fitting
 
-Coverage counts the number of held-out observations, out of 20, falling inside a program's 90% predictive interval (5th to 95th percentile), regardless of how much predictive density falls on each of them.
+| Setting | Value |
+| --- | --- |
+| Fitting rows | the 80 visible observations |
+| Chains | 1 |
+| Tuning iterations | 200 |
+| Retained posterior draws | 200 |
+| New observations simulated per draw and input | 20 |
+| Predictions per input | 4,000 |
+| Predictive interval used for coverage | 90%, from the 5th to the 95th percentile of the predictions |
+| Libraries | PyMC 5.21.1, PyTensor 2.28.3, ArviZ 0.23.4 |
 
 ## Configurations
 
@@ -66,7 +75,7 @@ Coverage counts the number of held-out observations, out of 20, falling inside a
 | `dugongs_predictive_curves`, `disease_spread_predictive_curves` | 1,210 (10 programs) and 968 (8 programs) | one program at one of 121 grid inputs | `program_id`, `role`, `row`, input, `predictive_q05`, `predictive_median`, `predictive_q95` (5th, 50th and 95th percentiles of the 4,000 predictions at that input) |
 | `dugongs_heldout_predictions`, `disease_spread_heldout_predictions` | 200 (10 programs) and 160 (8 programs) | one program at one held-out observation | the seven curve columns, `observed_length` or `observed_Infected_Count`, `covered` (True when the response lies between `predictive_q05` and `predictive_q95`), `heldout_log_score`, `avg_heldout_log_score` |
 
-In the held-out prediction files, `heldout_log_score` and `avg_heldout_log_score` are program-level values repeated on each row.
+In the held-out prediction files, each row refers to an observation, while `heldout_log_score` and `avg_heldout_log_score` refer to the program, so the same scores recur on its rows.
 
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