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diff --git a/LICENSE-data.md b/LICENSE-data.md
new file mode 100644
index 0000000000000000000000000000000000000000..a655589ac499ec4e81cc19168491295b16068172
--- /dev/null
+++ b/LICENSE-data.md
@@ -0,0 +1,10 @@
+# Licence for the data
+
+The data in `data/` (per-slide predictions and the data behind the paper's tables and figures) are licensed under the
+**Creative Commons Attribution 4.0 International** licence (CC BY 4.0).
+
+Full text: https://creativecommons.org/licenses/by/4.0/legalcode
+
+Please attribute CloudKites AI Lab and the paper "A small HER2 reader for routine H&E slides: real-world deployment"
+(Tran and Dang, 2026). The predictions refer to slides of public cohorts, whose own terms also apply: TCGA (NCI GDC open
+access), CPTAC-BRCA (open), Yale-HER2 (TCIA HER2-TUMOR-ROIS, CC BY 4.0).
diff --git a/LICENSE-weights.md b/LICENSE-weights.md
new file mode 100644
index 0000000000000000000000000000000000000000..7df7268252b603e03aa78836bedcc097dbad17a8
--- /dev/null
+++ b/LICENSE-weights.md
@@ -0,0 +1,12 @@
+# Licence for the model weights
+
+The model weights in `encoders/`, `checkpoints/` and `reader/` are licensed under the **Creative Commons
+Attribution-NonCommercial 4.0 International** licence (CC BY-NC 4.0).
+
+Full text: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+You may share and adapt the weights for non-commercial purposes, with attribution to CloudKites AI Lab and the paper
+"A small HER2 reader for routine H&E slides: real-world deployment" (Tran and Dang, 2026). Commercial use needs written
+permission from CloudKites.
+
+Research use only: the weights are not a medical device and must not be used for clinical decisions.
diff --git a/NOTICE b/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..39a4c7d7abdb533246e0d3aec2ea0524762b8bbe
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,9 @@
+This repository's encoders at stage 2 and later were trained by distillation from H-optimus-0 (Bioptimus), which is
+released under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0
+
+H-optimus-0 itself is not redistributed here. Our encoders are new weights (a ViT-S/16, 22M parameters) trained to match
+its outputs on TCGA tiles.
+
+Training and evaluation data: The Cancer Genome Atlas (NCI Genomic Data Commons, open access); CPTAC-BRCA (open);
+Yale-HER2 (The Cancer Imaging Archive, HER2-TUMOR-ROIS, CC BY 4.0). HEROHE (CC BY-NC-ND 3.0) and BCNB (non-commercial)
+were used for evaluation only, and no per-slide data from them are included.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..925d5108895f1f2514cd5b6ff5c8244780a45fb1
--- /dev/null
+++ b/README.md
@@ -0,0 +1,122 @@
+---
+license: cc-by-nc-4.0
+library_name: timm
+pipeline_tag: image-feature-extraction
+tags:
+  - pathology
+  - histopathology
+  - her2
+  - breast-cancer
+  - h-and-e
+  - vision-transformer
+  - multiple-instance-learning
+  - safetensors
+---
+
+# HER2 reader: a small ViT-S/16 encoder and HER2 slide classifier for routine H&E
+
+Model weights and output data behind the paper **"A small HER2 reader for routine H&E slides: real-world deployment"**
+(Thang Tran, Lan Dang, 2026; arXiv link to follow). We publish them so that anyone can check our numbers, rerun our
+evaluation and build on our encoder.
+
+**Research use only.** This is not a medical device and must not be used for clinical decisions. Our reader proposes
+a probability that a breast cancer is HER2-positive from its H&E slide, for a pathologist who reads it beside IHC and
+ISH, never instead of them.
+
+## What is in this repository
+
+| Path | What it holds |
+|---|---|
+| `encoders/` | ViT-S/16 tile encoders (22M parameters) at every training stage, with timm parameter names |
+| `checkpoints/` | the same models exactly as trained, in our original layout (including the self-distillation heads), for audit |
+| `reader/her2-jepa2-t2y-s4/` | the deployed reader: encoder, five CLAM-SB fold heads, and a manifest with architecture, tile recipe, operating point and validation |
+| `data/predictions/` | per-slide predictions, by seed: TCGA-BRCA out of fold, Yale-HER2 external, TCGA-BRCA + CPTAC-BRCA under the published protocol |
+| `data/paper/` | data behind the paper's tables and figures |
+| `CHECKSUMS.sha256` | SHA-256 of every file |
+
+### Encoders
+
+| File (`encoders/`) | Stage | TCGA-to-Yale AUC* |
+|---|---|---|
+| `stage1_ssl-vits16-final.safetensors` | self-supervised pretraining (DINOv2 + iBOT + KoLeo) on 98.6M TCGA tiles | 0.589 |
+| `stage2_distilled-hoptimus0-step25000.safetensors` | distilled from H-optimus-0 (CLS + 4x4 patch targets), step 25,000 | 0.762 |
+| `stage3a_jepa-round1.safetensors` | one JEPA refinement round | 0.798 |
+| `stage3b_jepa-round2_deployed-encoder.safetensors` | second JEPA round: **the deployed encoder** | **0.812** |
+| `r12a_distilled-1um-step25000.safetensors` | stage 3b distilled again at 1.0 um/px (not adopted) | 0.813** |
+| `r12b_distilled-1um-jepa6000.safetensors` | r12a plus one JEPA round (not adopted) | 0.812** |
+
+\* CLAM-SB reader trained on 490 TCGA-BRCA slides, tested on 192 Yale-HER2 slides, mean of 10 seeds (our protocol).
+\*\* Under the published reader's protocol (TCGA + CPTAC training, all tiles, 40 attention-MIL models); the deployed
+encoder scores 0.809 there.
+
+## Load an encoder (timm)
+
+```python
+import timm
+from safetensors.torch import load_file
+
+enc = timm.models.vision_transformer.VisionTransformer(
+    img_size=224, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
+    qkv_bias=True, init_values=1.0, class_token=True, no_embed_class=False, reg_tokens=0,
+    global_pool="token", fc_norm=False, num_classes=0)
+enc.load_state_dict(load_file("encoders/stage3b_jepa-round2_deployed-encoder.safetensors"), strict=True)
+enc.eval()
+# input: float32 [N, 3, 224, 224], RGB, ImageNet mean (0.485, 0.456, 0.406) and std (0.229, 0.224, 0.225)
+# output: [N, 384], the class token after the final LayerNorm
+```
+
+Tiles for our reader: 128 um squares read at 0.5 um/px (the coarsest pyramid level at or below 0.5 um/px), resized to
+224 px by nearest neighbour, tissue only; up to 400 tiles per slide, taken at an even stride across the section. The
+manifest in `reader/` gives every step.
+
+## The deployed reader
+
+Five CLAM-SB heads (5-fold cross-validation on 490 TCGA-BRCA slides, seed 4) read one bag of 400 tile vectors. The
+reader's probability is the mean of the five heads' softmax probabilities; at or above **0.27228** it proposes
+"positive". That threshold comes from TCGA-BRCA's out-of-fold predictions, and a laboratory should refit it on its own
+slides, because scores shift between hospitals.
+
+| Test set (never trained on) | AUC |
+|---|---|
+| Yale-HER2, 192 slides (5-model ensemble, seed 4) | 0.815 [0.753, 0.876] |
+| BCNB, 1,058 core biopsies (mean of 10 seeds) | 0.628 |
+| HEROHE test split, 150 slides (mean of 10 seeds) | 0.746 |
+| HEROHE IHC 2+ cases, 85 slides (amplified vs not) | 0.761 |
+
+At its threshold on Yale-HER2: sensitivity 0.72, specificity 0.79. On a 12-core CPU server it reads a slide in
+5.2 to 5.6 s.
+
+## Limitations
+
+- Its 490 TCGA-BRCA training slides were size-biased: negatives carry less tissue than positives. This inflates the
+  Yale AUC by about 0.04; on representative training sets our readers reach about 0.81.
+- Probabilities run low at hospitals other than TCGA's; set the threshold per laboratory.
+- On equivocal IHC 2+ cases (AUC 0.76 on HEROHE) it can help order and prioritise ISH. It does not replace ISH.
+- The best published reader (H-optimus-0, 1.1B parameters, Yale AUC 0.907) remains well ahead. The gap lies in the
+  encoder.
+
+## Training data and attributions
+
+- **Encoder:** TCGA diagnostic slides (NCI Genomic Data Commons, open access). Distillation teacher: H-optimus-0
+  (Bioptimus), Apache-2.0. See `NOTICE`.
+- **Reader heads:** 490 TCGA-BRCA slides with HER2 status from GDC clinical data.
+- **Evaluation cohorts:** Yale-HER2 (TCIA HER2-TUMOR-ROIS, CC BY 4.0), CPTAC-BRCA (open), HEROHE (CC BY-NC-ND 3.0,
+  evaluation only) and BCNB (non-commercial, evaluation only). Per-slide data for HEROHE and BCNB are not published
+  here; only aggregate numbers appear in the paper.
+
+## Licences
+
+- Model weights (`encoders/`, `checkpoints/`, `reader/`): **CC BY-NC 4.0** (`LICENSE-weights.md`).
+- Data (`data/`): **CC BY 4.0** (`LICENSE-data.md`).
+- The distillation teacher's Apache-2.0 notice: `NOTICE`.
+
+## Citation
+
+```bibtex
+@misc{tran2026her2reader,
+  title  = {A small HER2 reader for routine H\&E slides: real-world deployment},
+  author = {Tran, Thang and Dang, Lan},
+  year   = {2026},
+  note   = {Model weights and data: https://huggingface.co/cloudkites/her2-reader}
+}
+```
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+++ b/checkpoints/stage3a_jepa-round1.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5341f0f4c1c31b9f6cb8defa87217ac532c476f6d4602d189f23d2b4e809b8bf
+size 132895376
diff --git a/checkpoints/stage3b_jepa-round2_deployed-encoder.safetensors b/checkpoints/stage3b_jepa-round2_deployed-encoder.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..0e96ea2b4a63179b91b0d6d7fd5c067443d0b027
--- /dev/null
+++ b/checkpoints/stage3b_jepa-round2_deployed-encoder.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c00eacf7786ce44b421674bbd71713dddaf1e1b2efee1b23a8cab074a96f264b
+size 132895376
diff --git a/data/README.md b/data/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f55e379ddfbd4e538d7144b8f235fac9039a8b60
--- /dev/null
+++ b/data/README.md
@@ -0,0 +1,31 @@
+# Data
+
+## predictions/
+
+One folder per reader, then per training direction and seed:
+
+- `deployed-protocol_clam-sb_recipe-A/` — the deployed reader's protocol: CLAM-SB on 400 tiles at 0.5 um/px, our
+  deployed encoder. `t2y`: trained on TCGA-BRCA (490 slides), external test Yale-HER2 (192). `y2t`: trained on
+  Yale-HER2, external test TCGA-BRCA. Seeds 1-10; the deployed reader is `t2y/seed4`.
+- `r11o_published-protocol/` — the published reader's protocol with our deployed encoder: attention MIL with non-gated
+  attention on every tissue tile at 1.0 um/px, trained on TCGA-BRCA (917) + CPTAC-BRCA (382), external test Yale-HER2.
+  Seeds 1-8 (40 models).
+- `r12a_published-protocol/`, `r12b_published-protocol/` — the same protocol with the R12 encoders.
+
+Files in each seed folder:
+
+- `predictions.csv` — training cohort, 5-fold cross-validation: `fold, split (train/val/test), slide_id, case_id,
+  label, score`. Rows with `split == test` are the out-of-fold predictions.
+- `external_predictions.csv` — every fold model's score on every external slide: `fold, split (external), slide_id,
+  case_id, label, score`. The seed's reader is the mean over its five folds.
+- `results.csv` — per-fold, pooled and external-ensemble metrics (AUC with DeLong and bootstrap intervals, threshold,
+  sensitivity, specificity, PPV, NPV).
+- `folds.csv` — which fold each slide's case was dealt into (case-stratified, fold seed 1337).
+
+`label` is HER2 status (1 positive, 0 negative): for TCGA-BRCA from GDC clinical data, for CPTAC-BRCA from its
+proteogenomic ERBB2 status, for Yale-HER2 from the collection's case labels. `score` is the probability of HER2-positive.
+
+## paper/
+
+`paper-data/` holds the data files our paper's build reads (tables, figure data, macros); `book-data/` holds the
+files it reads from our technical report's data folder.
diff --git a/data/paper/book-data/auc_dens.dat b/data/paper/book-data/auc_dens.dat
new file mode 100644
index 0000000000000000000000000000000000000000..21703c16dccd117a6baa466fdc8834c8021013f7
--- /dev/null
+++ b/data/paper/book-data/auc_dens.dat
@@ -0,0 +1,112 @@
+x neg p70 p95
+-4.00 0.0001 0.0000 0.0000
+-3.90 0.0002 0.0000 0.0000
+-3.80 0.0003 0.0000 0.0000
+-3.70 0.0004 0.0000 0.0000
+-3.60 0.0006 0.0000 0.0000
+-3.50 0.0009 0.0000 0.0000
+-3.40 0.0012 0.0001 0.0000
+-3.30 0.0017 0.0001 0.0000
+-3.20 0.0024 0.0002 0.0000
+-3.10 0.0033 0.0002 0.0000
+-3.00 0.0044 0.0004 0.0000
+-2.90 0.0060 0.0005 0.0000
+-2.80 0.0079 0.0008 0.0000
+-2.70 0.0104 0.0011 0.0000
+-2.60 0.0136 0.0015 0.0000
+-2.50 0.0175 0.0021 0.0000
+-2.40 0.0224 0.0029 0.0000
+-2.30 0.0283 0.0039 0.0000
+-2.20 0.0355 0.0053 0.0000
+-2.10 0.0440 0.0070 0.0000
+-2.00 0.0540 0.0093 0.0000
+-1.90 0.0656 0.0122 0.0001
+-1.80 0.0790 0.0158 0.0001
+-1.70 0.0940 0.0202 0.0001
+-1.60 0.1109 0.0257 0.0002
+-1.50 0.1295 0.0323 0.0003
+-1.40 0.1497 0.0403 0.0004
+-1.30 0.1714 0.0496 0.0006
+-1.20 0.1942 0.0606 0.0008
+-1.10 0.2179 0.0732 0.0011
+-1.00 0.2420 0.0875 0.0016
+-0.90 0.2661 0.1037 0.0022
+-0.80 0.2897 0.1216 0.0030
+-0.70 0.3123 0.1411 0.0041
+-0.60 0.3332 0.1622 0.0055
+-0.50 0.3521 0.1846 0.0074
+-0.40 0.3683 0.2079 0.0097
+-0.30 0.3814 0.2319 0.0127
+-0.20 0.3910 0.2561 0.0164
+-0.10 0.3970 0.2800 0.0210
+0.00 0.3989 0.3030 0.0267
+0.10 0.3970 0.3247 0.0335
+0.20 0.3910 0.3445 0.0416
+0.30 0.3814 0.3619 0.0512
+0.40 0.3683 0.3763 0.0624
+0.50 0.3521 0.3875 0.0753
+0.60 0.3332 0.3950 0.0899
+0.70 0.3123 0.3986 0.1063
+0.80 0.2897 0.3983 0.1245
+0.90 0.2661 0.3940 0.1443
+1.00 0.2420 0.3858 0.1656
+1.10 0.2179 0.3741 0.1881
+1.20 0.1942 0.3592 0.2116
+1.30 0.1714 0.3414 0.2356
+1.40 0.1497 0.3212 0.2598
+1.50 0.1295 0.2992 0.2836
+1.60 0.1109 0.2760 0.3065
+1.70 0.0940 0.2520 0.3279
+1.80 0.0790 0.2279 0.3474
+1.90 0.0656 0.2040 0.3643
+2.00 0.0540 0.1807 0.3783
+2.10 0.0440 0.1586 0.3889
+2.20 0.0355 0.1377 0.3958
+2.30 0.0283 0.1185 0.3988
+2.40 0.0224 0.1009 0.3979
+2.50 0.0175 0.0850 0.3930
+2.60 0.0136 0.0710 0.3843
+2.70 0.0104 0.0586 0.3720
+2.80 0.0079 0.0480 0.3566
+2.90 0.0060 0.0388 0.3384
+3.00 0.0044 0.0311 0.3179
+3.10 0.0033 0.0247 0.2957
+3.20 0.0024 0.0194 0.2723
+3.30 0.0017 0.0151 0.2483
+3.40 0.0012 0.0117 0.2241
+3.50 0.0009 0.0089 0.2003
+3.60 0.0006 0.0067 0.1772
+3.70 0.0004 0.0050 0.1553
+3.80 0.0003 0.0037 0.1347
+3.90 0.0002 0.0027 0.1156
+4.00 0.0001 0.0020 0.0983
+4.10 0.0001 0.0014 0.0827
+4.20 0.0001 0.0010 0.0689
+4.30 0.0000 0.0007 0.0569
+4.40 0.0000 0.0005 0.0465
+4.50 0.0000 0.0003 0.0376
+4.60 0.0000 0.0002 0.0301
+4.70 0.0000 0.0002 0.0238
+4.80 0.0000 0.0001 0.0187
+4.90 0.0000 0.0001 0.0145
+5.00 0.0000 0.0000 0.0112
+5.10 0.0000 0.0000 0.0085
+5.20 0.0000 0.0000 0.0064
+5.30 0.0000 0.0000 0.0048
+5.40 0.0000 0.0000 0.0035
+5.50 0.0000 0.0000 0.0026
+5.60 0.0000 0.0000 0.0019
+5.70 0.0000 0.0000 0.0013
+5.80 0.0000 0.0000 0.0010
+5.90 0.0000 0.0000 0.0007
+6.00 0.0000 0.0000 0.0005
+6.10 0.0000 0.0000 0.0003
+6.20 0.0000 0.0000 0.0002
+6.30 0.0000 0.0000 0.0001
+6.40 0.0000 0.0000 0.0001
+6.50 0.0000 0.0000 0.0001
+6.60 0.0000 0.0000 0.0000
+6.70 0.0000 0.0000 0.0000
+6.80 0.0000 0.0000 0.0000
+6.90 0.0000 0.0000 0.0000
+7.00 0.0000 0.0000 0.0000
diff --git a/data/paper/book-data/her2_bars_arms.dat b/data/paper/book-data/her2_bars_arms.dat
new file mode 100644
index 0000000000000000000000000000000000000000..75713bd0ed97ccff5780d97dfe8d81daa60e3646
--- /dev/null
+++ b/data/paper/book-data/her2_bars_arms.dat
@@ -0,0 +1,5 @@
+x cell d15rand d15rand_sd d15s50k d15s50k_sd d15final d15final_sd d16rand d16rand_sd d16s50k d16s50k_sd d16final d16final_sd s16s50 s16s50_sd s16final s16final_sd
+1 yy 0.7616 0.0241 0.7718 0.0161 0.7923 0.0214 0.7922 0.0088 0.8143 0.0083 0.8196 0.0197 0.7808 0.0174 0.7675 0.0267
+2 tt 0.5474 0.0242 0.5688 0.0093 0.5878 0.0072 0.5922 0.0143 0.6361 0.0038 0.6228 0.0229 0.6085 0.0042 0.5891 0.0128
+3 ty 0.6205 0.0347 0.7244 0.0175 0.6512 0.0293 0.7387 0.0281 0.7393 0.0173 0.7443 0.0219 0.5499 0.0167 0.5841 0.0385
+4 yt 0.5288 0.0026 0.5576 0.0113 0.5526 0.0017 0.5632 0.0016 0.6215 0.0038 0.5930 0.0059 0.5281 0.0060 0.5205 0.0029
diff --git a/data/paper/book-data/her2_bars_august.dat b/data/paper/book-data/her2_bars_august.dat
new file mode 100644
index 0000000000000000000000000000000000000000..401d5dade7e5ed4a9c24e1c270db0c93254f7499
--- /dev/null
+++ b/data/paper/book-data/her2_bars_august.dat
@@ -0,0 +1,5 @@
+x cell hoptaug hoptaug_sd phikon phikon_sd virchow2 virchow2_sd hibou hibou_sd rn50 rn50_sd
+1 yy 0.8978 0.0104 0.8756 0.0047 0.8673 0.0086 0.7794 0.0119 0.7009 0.0427
+2 tt 0.6685 0.0232 0.6690 0.0085 0.6336 0.0146 0.6442 0.0161 0.5517 0.0279
+3 ty 0.7977 0.0172 0.8128 0.0191 0.7425 0.0349 0.7172 0.0299 0.6296 0.0149
+4 yt 0.6562 0.0013 0.6606 0.0003 0.6541 0.0058 0.6309 0.0105 0.5606 0.0069
diff --git a/data/paper/book-data/her2_bars_d18.dat b/data/paper/book-data/her2_bars_d18.dat
new file mode 100644
index 0000000000000000000000000000000000000000..7266a5f48a21d245bcf32d1ce9fbe209a45480ed
--- /dev/null
+++ b/data/paper/book-data/her2_bars_d18.dat
@@ -0,0 +1,5 @@
+x cell b16s40 b16s40_sd d18s40k d18s40k_sd d18s45k d18s45k_sd d18s50k d18s50k_sd d16final d16final_sd phikon phikon_sd hoptcard hoptcard_sd
+1 yy 0.7934 0.0135 0.8177 0.0098 0.7765 0.0094 0.8032 0.0397 0.8196 0.0197 0.8756 0.0047 0.8868 0.0072
+2 tt 0.6074 0.0161 0.6332 0.0157 0.6329 0.0143 0.6148 0.0022 0.6228 0.0229 0.6690 0.0085 0.6755 0.0162
+3 ty 0.6651 0.0377 0.7757 0.0098 0.7623 0.0040 0.7547 0.0075 0.7443 0.0219 0.8128 0.0191 0.8100 0.0116
+4 yt 0.5472 0.0057 0.6406 0.0080 0.6215 0.0069 0.6136 0.0012 0.5930 0.0059 0.6606 0.0003 0.6531 0.0078
diff --git a/data/paper/book-data/her2_bars_refchecks.dat b/data/paper/book-data/her2_bars_refchecks.dat
new file mode 100644
index 0000000000000000000000000000000000000000..ff8a7b14784f00bee298e563a137dc60b19ac746
--- /dev/null
+++ b/data/paper/book-data/her2_bars_refchecks.dat
@@ -0,0 +1,5 @@
+x cell hoptaug hoptaug_sd hoptrep hoptrep_sd hoptcard hoptcard_sd hoptcm hoptcm_sd hopt1um hopt1um_sd midnight midnight_sd midnightcm midnightcm_sd midnight1um midnight1um_sd
+1 yy 0.8978 0.0104 0.8954 0.0143 0.8868 0.0072 0.8925 0.0157 0.9088 0.0066 0.8064 0.0091 0.8114 0.0112 0.8013 0.0060
+2 tt 0.6685 0.0232 0.6683 0.0145 0.6755 0.0162 0.6723 0.0179 0.6591 0.0153 0.5970 0.0028 0.6105 0.0084 0.5858 0.0127
+3 ty 0.7977 0.0172 0.7871 0.0310 0.8100 0.0116 0.8028 0.0253 0.8033 0.0109 0.7163 0.0086 0.7337 0.0209 0.7243 0.0269
+4 yt 0.6562 0.0013 0.6562 0.0052 0.6531 0.0078 0.6630 0.0057 0.6419 0.0071 0.6240 0.0039 0.6376 0.0039 0.6087 0.0054
diff --git a/data/paper/book-data/her2_bars_students.dat b/data/paper/book-data/her2_bars_students.dat
new file mode 100644
index 0000000000000000000000000000000000000000..b79d1cd66b7c22220cf3fbe3e181e94c15d359a0
--- /dev/null
+++ b/data/paper/book-data/her2_bars_students.dat
@@ -0,0 +1,5 @@
+x cell s16s50 s16s50_sd d13 d13_sd d14 d14_sd midnight midnight_sd hoptcard hoptcard_sd
+1 yy 0.7808 0.0174 0.7714 0.0195 0.8064 0.0183 0.8064 0.0091 0.8868 0.0072
+2 tt 0.6085 0.0042 0.5899 0.0170 0.6688 0.0101 0.5970 0.0028 0.6755 0.0162
+3 ty 0.5499 0.0167 0.7010 0.0206 0.7157 0.0151 0.7163 0.0086 0.8100 0.0116
+4 yt 0.5281 0.0060 0.5454 0.0081 0.5733 0.0040 0.6240 0.0039 0.6531 0.0078
diff --git a/data/paper/book-data/her2_dot.dat b/data/paper/book-data/her2_dot.dat
new file mode 100644
index 0000000000000000000000000000000000000000..1f913195e8f34bb302b69b17f2bf512b7131a927
--- /dev/null
+++ b/data/paper/book-data/her2_dot.dat
@@ -0,0 +1,39 @@
+y key group yy yy_sd yy_lo yy_hi tt tt_sd tt_lo tt_hi ty ty_sd ty_lo ty_hi yt yt_sd yt_lo yt_hi
+2 vits16-random-init ctl 0.5900 0.0040 0.5873 0.5947 0.5080 0.0124 0.4989 0.5221 0.4375 0.0317 0.4041 0.4673 0.4849 0.0020 0.4835 0.4873
+3 dinov2-vits14 ctl 0.7192 0.0203 0.6988 0.7394 0.5011 0.0226 0.4878 0.5272 0.5889 0.0203 0.5659 0.6044 0.5764 0.0115 0.5631 0.5834
+4 resnet50-imagenet ctl 0.7009 0.0427 0.6559 0.7408 0.5517 0.0279 0.5315 0.5835 0.6296 0.0149 0.6124 0.6388 0.5606 0.0069 0.5539 0.5677
+6.6 ssl-vits16-step5k s16 0.7264 0.0064 0.7191 0.7310 0.5697 0.0129 0.5549 0.5778 0.6764 0.0184 0.6556 0.6903 0.5228 0.0041 0.5187 0.5269
+7.6 ssl-vits16-step20k s16 0.7382 0.0126 0.7285 0.7525 0.5675 0.0106 0.5602 0.5797 0.6174 0.0430 0.5685 0.6490 0.5451 0.0010 0.5444 0.5463
+8.6 ssl-vits16-step35k s16 0.8039 0.0106 0.7919 0.8120 0.5992 0.0082 0.5898 0.6047 0.5613 0.0149 0.5464 0.5763 0.5306 0.0038 0.5262 0.5333
+9.6 ssl-vits16-step40k s16 0.7919 0.0193 0.7696 0.8035 0.6199 0.0090 0.6102 0.6278 0.5651 0.0174 0.5469 0.5814 0.5144 0.0034 0.5107 0.5173
+10.6 ssl-vits16-step45k s16 0.7601 0.0245 0.7400 0.7873 0.5887 0.0192 0.5666 0.6017 0.5873 0.0660 0.5455 0.6633 0.5253 0.0061 0.5190 0.5312
+11.6 ssl-vits16-step50k s16 0.7808 0.0174 0.7611 0.7939 0.6085 0.0042 0.6036 0.6110 0.5499 0.0167 0.5307 0.5603 0.5281 0.0060 0.5233 0.5348
+12.6 ssl-vits16-step55k s16 0.7880 0.0285 0.7561 0.8109 0.5888 0.0031 0.5854 0.5915 0.6004 0.0520 0.5576 0.6583 0.5322 0.0064 0.5260 0.5387
+13.6 ssl-vits16-step60k s16 0.7775 0.0090 0.7681 0.7860 0.5958 0.0062 0.5887 0.5999 0.5922 0.0314 0.5560 0.6112 0.5318 0.0034 0.5296 0.5358
+14.6 ssl-vits16-step65k s16 0.7588 0.0154 0.7486 0.7765 0.6211 0.0165 0.6104 0.6402 0.5530 0.0541 0.4957 0.6031 0.5177 0.0018 0.5157 0.5192
+15.6 ssl-vits16-final s16 0.7675 0.0267 0.7376 0.7892 0.5891 0.0128 0.5754 0.6009 0.5841 0.0385 0.5417 0.6167 0.5205 0.0029 0.5182 0.5238
+18.2 ssl-vitb16-step10k b16 0.7575 0.0358 0.7255 0.7961 0.5877 0.0045 0.5839 0.5927 0.5724 0.0202 0.5527 0.5930 0.5307 0.0105 0.5201 0.5411
+19.2 ssl-vitb16-step35k b16 0.8290 0.0099 0.8217 0.8403 0.5819 0.0184 0.5690 0.6030 0.6380 0.0233 0.6212 0.6646 0.5455 0.0017 0.5444 0.5474
+20.2 ssl-vitb16-step40k b16 0.7934 0.0135 0.7781 0.8035 0.6074 0.0161 0.5909 0.6231 0.6651 0.0377 0.6307 0.7053 0.5472 0.0057 0.5427 0.5536
+22.8 distill-midnight-20k dst 0.7714 0.0195 0.7494 0.7869 0.5899 0.0170 0.5704 0.6005 0.7010 0.0206 0.6799 0.7211 0.5454 0.0081 0.5373 0.5535
+23.8 distill-hoptimus-20k dst 0.8064 0.0183 0.7861 0.8215 0.6688 0.0101 0.6591 0.6793 0.7157 0.0151 0.7056 0.7330 0.5733 0.0040 0.5704 0.5778
+24.8 distill-midnight-5k-from-random dst 0.7616 0.0241 0.7352 0.7823 0.5474 0.0242 0.5200 0.5659 0.6205 0.0347 0.5811 0.6464 0.5288 0.0026 0.5260 0.5311
+25.8 distill-midnight-5k-from-vits16-step50k dst 0.7718 0.0161 0.7532 0.7811 0.5688 0.0093 0.5582 0.5761 0.7244 0.0175 0.7071 0.7422 0.5576 0.0113 0.5448 0.5659
+26.8 distill-midnight-5k-from-vits16-final dst 0.7923 0.0214 0.7697 0.8122 0.5878 0.0072 0.5804 0.5949 0.6512 0.0293 0.6178 0.6725 0.5526 0.0017 0.5516 0.5546
+27.8 distill-hoptimus-5k-from-random dst 0.7922 0.0088 0.7849 0.8019 0.5922 0.0143 0.5810 0.6083 0.7387 0.0281 0.7071 0.7607 0.5632 0.0016 0.5618 0.5649
+28.8 distill-hoptimus-5k-from-vits16-step50k dst 0.8143 0.0083 0.8089 0.8238 0.6361 0.0038 0.6322 0.6398 0.7393 0.0173 0.7215 0.7562 0.6215 0.0038 0.6186 0.6258
+29.8 distill-hoptimus-5k-from-vits16-final dst 0.8196 0.0197 0.7974 0.8350 0.6228 0.0229 0.5982 0.6435 0.7443 0.0219 0.7197 0.7616 0.5930 0.0059 0.5864 0.5976
+32.4 distill-hoptimus-vitb16-5k-from-vitb16-step40k dstb 0.8177 0.0098 0.8110 0.8290 0.6332 0.0157 0.6211 0.6509 0.7757 0.0098 0.7644 0.7826 0.6406 0.0080 0.6328 0.6488
+33.4 distill-hoptimus-vitb16-5k-from-vitb16-step45k dstb 0.7765 0.0094 0.7681 0.7867 0.6329 0.0143 0.6170 0.6444 0.7623 0.0040 0.7578 0.7654 0.6215 0.0069 0.6153 0.6289
+34.4 distill-hoptimus-vitb16-5k-from-vitb16-step50k dstb 0.8032 0.0397 0.7664 0.8453 0.6148 0.0022 0.6130 0.6173 0.7547 0.0075 0.7470 0.7619 0.6136 0.0012 0.6127 0.6149
+37 hibou-b ref 0.7794 0.0119 0.7684 0.7920 0.6442 0.0161 0.6262 0.6569 0.7172 0.0299 0.6932 0.7506 0.6309 0.0105 0.6187 0.6374
+38 phikon ref 0.8756 0.0047 0.8706 0.8800 0.6690 0.0085 0.6613 0.6780 0.8128 0.0191 0.7914 0.8278 0.6606 0.0003 0.6602 0.6609
+39 virchow2 ref 0.8673 0.0086 0.8576 0.8742 0.6336 0.0146 0.6247 0.6505 0.7425 0.0349 0.7089 0.7785 0.6541 0.0058 0.6494 0.6605
+40 midnight12k ref 0.8064 0.0091 0.7960 0.8129 0.5970 0.0028 0.5944 0.6001 0.7163 0.0086 0.7107 0.7262 0.6240 0.0039 0.6195 0.6267
+41 midnight12k-cls-mean ref 0.8114 0.0112 0.7993 0.8213 0.6105 0.0084 0.6016 0.6184 0.7337 0.0209 0.7170 0.7571 0.6376 0.0039 0.6334 0.6411
+42 midnight12k-1.0um ref 0.8013 0.0060 0.7953 0.8073 0.5858 0.0127 0.5723 0.5976 0.7243 0.0269 0.6932 0.7400 0.6087 0.0054 0.6054 0.6150
+43 hoptimus0-imagenetnorm ref 0.8978 0.0104 0.8862 0.9063 0.6685 0.0232 0.6442 0.6905 0.7977 0.0172 0.7786 0.8121 0.6562 0.0013 0.6549 0.6575
+44 hoptimus0-imagenetnorm-repeat ref 0.8954 0.0143 0.8793 0.9066 0.6683 0.0145 0.6515 0.6771 0.7871 0.0310 0.7534 0.8144 0.6562 0.0052 0.6520 0.6621
+45 hoptimus0-cardnorm ref 0.8868 0.0072 0.8792 0.8935 0.6755 0.0162 0.6632 0.6938 0.8100 0.0116 0.7986 0.8218 0.6531 0.0078 0.6445 0.6596
+46 hoptimus0-cls-mean ref 0.8925 0.0157 0.8747 0.9043 0.6723 0.0179 0.6553 0.6909 0.8028 0.0253 0.7736 0.8194 0.6630 0.0057 0.6566 0.6674
+47 hoptimus0-1.0um ref 0.9088 0.0066 0.9038 0.9163 0.6591 0.0153 0.6415 0.6696 0.8033 0.0109 0.7925 0.8143 0.6419 0.0071 0.6348 0.6491
diff --git a/data/paper/book-data/her2_dot_rows.dat b/data/paper/book-data/her2_dot_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..a7970a778d94852223dd8c42cc71074aeafd2928
--- /dev/null
+++ b/data/paper/book-data/her2_dot_rows.dat
@@ -0,0 +1,45 @@
+y	label
+1	\textbf{Controls}
+2	random weights, ViT-S/16
+3	DINOv2 ViT-S/14 (natural images)
+4	ResNet-50, ImageNet (Aug.)
+5.6	\textbf{Ours: ssl-vits16}
+6.6	ssl-vits16 step 5k
+7.6	ssl-vits16 step 20k
+8.6	ssl-vits16 step 35k
+9.6	ssl-vits16 step 40k
+10.6	ssl-vits16 step 45k
+11.6	ssl-vits16 step 50k
+12.6	ssl-vits16 step 55k
+13.6	ssl-vits16 step 60k
+14.6	ssl-vits16 step 65k
+15.6	ssl-vits16 final (93k)
+17.2	\textbf{Ours: ssl-vitb16}
+18.2	ssl-vitb16 step 10k
+19.2	ssl-vitb16 step 35k
+20.2	ssl-vitb16 step 40k
+21.8	\textbf{Ours: distilled students}
+22.8	D13: Midnight-12k teacher, 20k steps
+23.8	D14: H-optimus-0 teacher, 20k steps
+24.8	D15: Midnight, 5k, from random
+25.8	D15: Midnight, 5k, from step 50k
+26.8	D15: Midnight, 5k, from final
+27.8	D16: H-optimus-0, 5k, from random
+28.8	D16: H-optimus-0, 5k, from step 50k
+29.8	\textbf{\textcolor{hrBest}{D16: H-optimus-0, 5k, from final}}
+31.4	\textbf{Ours: ViT-B/16 students}
+32.4	D18: H-optimus-0, ViT-B/16, 5k, from step 40k
+33.4	D18: H-optimus-0, ViT-B/16, 5k, from step 45k
+34.4	D18: H-optimus-0, ViT-B/16, 5k, from step 50k
+36	\textbf{Released encoders}
+37	Hibou-B (Aug.)
+38	Phikon v1, ViT-B/16 (Aug.)
+39	Virchow2 (Aug.)
+40	Midnight-12k, CLS
+41	Midnight-12k, CLS+mean
+42	Midnight-12k, CLS, 1.0\,\textmu m/px
+43	H-optimus-0, ImageNet norm. (Aug.)
+44	H-optimus-0, ImageNet norm. (Sep.)
+45	H-optimus-0, card norm., CLS
+46	H-optimus-0, card norm., CLS+mean
+47	H-optimus-0, card norm., CLS, 1.0\,\textmu m/px
diff --git a/data/paper/book-data/her2_leader.dat b/data/paper/book-data/her2_leader.dat
new file mode 100644
index 0000000000000000000000000000000000000000..3af70024672cd4b9b5cee04d37bd7a55887084ed
--- /dev/null
+++ b/data/paper/book-data/her2_leader.dat
@@ -0,0 +1,12 @@
+y key cls params gmacs cpu400 yy yy_sd yy_rank tt tt_sd tt_rank ty ty_sd ty_rank yt yt_sd yt_rank
+1 phikon ref 8.57979e+07 17.56 22.98 0.8756 0.0047 2 0.6690 0.0085 2 0.8128 0.0191 1 0.6606 0.0003 1
+2 hoptimus0-cardnorm ref 1.13465e+09 304.17 312.29 0.8868 0.0072 1 0.6755 0.0162 1 0.8100 0.0116 2 0.6531 0.0078 3
+3 distill-hoptimus-vitb16-5k-from-vitb16-step40k ours 8.57979e+07 17.56 22.98 0.8177 0.0098 5 0.6332 0.0157 5 0.7757 0.0098 3 0.6406 0.0080 4
+4 distill-hoptimus-5k-from-vits16-final best 2.16653e+07 4.60 8.43 0.8196 0.0197 4 0.6228 0.0229 6 0.7443 0.0219 4 0.5930 0.0059 7
+5 virchow2 ref 6.31151e+08 170.07 nan 0.8673 0.0086 3 0.6336 0.0146 4 0.7425 0.0349 5 0.6541 0.0058 2
+6 midnight12k-cls-mean ref 1.13465e+09 299.39 312.29 0.8114 0.0112 6 0.6105 0.0084 7 0.7337 0.0209 6 0.6376 0.0039 5
+7 hibou-b ref 8.57211e+07 23.54 nan 0.7794 0.0119 7 0.6442 0.0161 3 0.7172 0.0299 7 0.6309 0.0105 6
+8 distill-midnight-20k ours 2.16653e+07 4.60 8.43 0.7714 0.0195 8 0.5899 0.0170 8 0.7010 0.0206 8 0.5454 0.0081 10
+9 resnet50-imagenet ctl 2.56e+07 4.10 nan 0.7009 0.0427 11 0.5517 0.0279 10 0.6296 0.0149 9 0.5606 0.0069 9
+10 dinov2-vits14 ctl 2.16192e+07 6.12 nan 0.7192 0.0203 10 0.5011 0.0226 11 0.5889 0.0203 10 0.5764 0.0115 8
+11 ssl-vits16-final ours 2.16653e+07 4.60 8.43 0.7675 0.0267 9 0.5891 0.0128 9 0.5841 0.0385 11 0.5205 0.0029 11
diff --git a/data/paper/book-data/her2_leader_rows.dat b/data/paper/book-data/her2_leader_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..d47cccd604271347bd8296cd63b3eb685e0a0ee8
--- /dev/null
+++ b/data/paper/book-data/her2_leader_rows.dat
@@ -0,0 +1,12 @@
+y	label
+1	Phikon (86M)
+2	H-optimus-0 (1.1B)
+3	Ours, D18: ViT-B student of H-optimus-0 (86M)
+4	\textbf{Ours, D16: student of H-optimus-0} (22M)
+5	Virchow2 (631M)
+6	Midnight-12k (1.1B)
+7	Hibou-B (86M)
+8	Ours, D13: student of Midnight-12k (22M)
+9	ResNet-50 (26M)
+10	DINOv2 ViT-S/14 (22M)
+11	Ours, ssl-vits16: self-supervised (22M)
diff --git a/data/paper/book-data/her2_macros.tex b/data/paper/book-data/her2_macros.tex
new file mode 100644
index 0000000000000000000000000000000000000000..6d7d26d9d5245eeeb530f08019e83be7f2e8a0d3
--- /dev/null
+++ b/data/paper/book-data/her2_macros.tex
@@ -0,0 +1,2112 @@
+% written by her2_data.py from data/her2_raw; do not edit
+\herset{updated}{2 October 2026}
+\herset{n-encoders}{38}
+\herset{n-runs}{228}
+\herset{rand-yy}{0.590}
+\herset{rand-yy-sd}{0.004}
+\herset{rand-yy-v}{0.5900}
+\herset{rand-yy-mf}{0.602}
+\herset{rand-yy-mfsd}{0.010}
+\herset{rand-yy-s1}{0.588}
+\herset{rand-yy-s2}{0.587}
+\herset{rand-yy-s3}{0.595}
+\herset{rand-tt}{0.508}
+\herset{rand-tt-sd}{0.012}
+\herset{rand-tt-v}{0.5080}
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+\herset{rand-tt-mfsd}{0.014}
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+\herset{rand-tt-s3}{0.499}
+\herset{rand-ty}{0.438}
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+\herset{pd-d18s50k-d16final-ty}{+0.010}
+\herset{pd-d18s50k-d16final-ty-k}{3/3}
+\herset{pd-d18s50k-d16final-yt}{+0.021}
+\herset{pd-d18s50k-d16final-yt-k}{3/3}
+\herset{pd-hoptrep-hoptaug-yy}{$-$0.002}
+\herset{pd-hoptrep-hoptaug-yy-k}{1/3}
+\herset{pd-hoptrep-hoptaug-tt}{0.000}
+\herset{pd-hoptrep-hoptaug-tt-k}{1/3}
+\herset{pd-hoptrep-hoptaug-ty}{$-$0.011}
+\herset{pd-hoptrep-hoptaug-ty-k}{1/3}
+\herset{pd-hoptrep-hoptaug-yt}{0.000}
+\herset{pd-hoptrep-hoptaug-yt-k}{1/3}
+\herset{pd-hoptcard-hoptrep-yy}{$-$0.009}
+\herset{pd-hoptcard-hoptrep-yy-k}{0/3}
+\herset{pd-hoptcard-hoptrep-tt}{+0.007}
+\herset{pd-hoptcard-hoptrep-tt-k}{2/3}
+\herset{pd-hoptcard-hoptrep-ty}{+0.023}
+\herset{pd-hoptcard-hoptrep-ty-k}{2/3}
+\herset{pd-hoptcard-hoptrep-yt}{$-$0.003}
+\herset{pd-hoptcard-hoptrep-yt-k}{1/3}
+\herset{pd-hoptcm-hoptcard-yy}{+0.006}
+\herset{pd-hoptcm-hoptcard-yy-k}{2/3}
+\herset{pd-hoptcm-hoptcard-tt}{$-$0.003}
+\herset{pd-hoptcm-hoptcard-tt-k}{1/3}
+\herset{pd-hoptcm-hoptcard-ty}{$-$0.007}
+\herset{pd-hoptcm-hoptcard-ty-k}{1/3}
+\herset{pd-hoptcm-hoptcard-yt}{+0.010}
+\herset{pd-hoptcm-hoptcard-yt-k}{3/3}
+\herset{pd-hopt1um-hoptcard-yy}{+0.022}
+\herset{pd-hopt1um-hoptcard-yy-k}{3/3}
+\herset{pd-hopt1um-hoptcard-tt}{$-$0.016}
+\herset{pd-hopt1um-hoptcard-tt-k}{1/3}
+\herset{pd-hopt1um-hoptcard-ty}{$-$0.007}
+\herset{pd-hopt1um-hoptcard-ty-k}{1/3}
+\herset{pd-hopt1um-hoptcard-yt}{$-$0.011}
+\herset{pd-hopt1um-hoptcard-yt-k}{0/3}
+\herset{pd-midnightcm-midnight-yy}{+0.005}
+\herset{pd-midnightcm-midnight-yy-k}{2/3}
+\herset{pd-midnightcm-midnight-tt}{+0.013}
+\herset{pd-midnightcm-midnight-tt-k}{3/3}
+\herset{pd-midnightcm-midnight-ty}{+0.017}
+\herset{pd-midnightcm-midnight-ty-k}{2/3}
+\herset{pd-midnightcm-midnight-yt}{+0.014}
+\herset{pd-midnightcm-midnight-yt-k}{3/3}
+\herset{pd-midnight1um-midnight-yy}{$-$0.005}
+\herset{pd-midnight1um-midnight-yy-k}{1/3}
+\herset{pd-midnight1um-midnight-tt}{$-$0.011}
+\herset{pd-midnight1um-midnight-tt-k}{1/3}
+\herset{pd-midnight1um-midnight-ty}{+0.008}
+\herset{pd-midnight1um-midnight-ty-k}{2/3}
+\herset{pd-midnight1um-midnight-yt}{$-$0.015}
+\herset{pd-midnight1um-midnight-yt-k}{0/3}
+\herset{pd-hoptcard-midnightcm-yy}{+0.075}
+\herset{pd-hoptcard-midnightcm-yy-k}{3/3}
+\herset{pd-hoptcard-midnightcm-tt}{+0.065}
+\herset{pd-hoptcard-midnightcm-tt-k}{3/3}
+\herset{pd-hoptcard-midnightcm-ty}{+0.076}
+\herset{pd-hoptcard-midnightcm-ty-k}{3/3}
+\herset{pd-hoptcard-midnightcm-yt}{+0.016}
+\herset{pd-hoptcard-midnightcm-yt-k}{3/3}
+\herset{gap-d18s40k-yy}{26}
+\herset{gap-d18s40k-tt}{38}
+\herset{gap-d18s40k-ty}{76}
+\herset{gap-d18s40k-yt}{88}
+\herset{gap-d16final-yy}{44}
+\herset{gap-d16final-tt}{39}
+\herset{gap-d16final-ty}{71}
+\herset{gap-d16final-yt}{55}
+\herset{rand-costyale}{0.152}
+\herset{rand-costtcga}{0.023}
+\herset{dinov2s-costyale}{0.130}
+\herset{dinov2s-costtcga}{-0.075}
+\herset{rn50-costyale}{0.071}
+\herset{rn50-costtcga}{-0.009}
+\herset{s16s5-costyale}{0.050}
+\herset{s16s5-costtcga}{0.047}
+\herset{s16s20-costyale}{0.121}
+\herset{s16s20-costtcga}{0.022}
+\herset{s16s35-costyale}{0.243}
+\herset{s16s35-costtcga}{0.069}
+\herset{s16s40-costyale}{0.227}
+\herset{s16s40-costtcga}{0.106}
+\herset{s16s45-costyale}{0.173}
+\herset{s16s45-costtcga}{0.063}
+\herset{s16s50-costyale}{0.231}
+\herset{s16s50-costtcga}{0.080}
+\herset{s16s55-costyale}{0.188}
+\herset{s16s55-costtcga}{0.057}
+\herset{s16s60-costyale}{0.185}
+\herset{s16s60-costtcga}{0.064}
+\herset{s16s65-costyale}{0.206}
+\herset{s16s65-costtcga}{0.103}
+\herset{s16final-costyale}{0.183}
+\herset{s16final-costtcga}{0.069}
+\herset{b16s10-costyale}{0.185}
+\herset{b16s10-costtcga}{0.057}
+\herset{b16s35-costyale}{0.191}
+\herset{b16s35-costtcga}{0.036}
+\herset{b16s40-costyale}{0.128}
+\herset{b16s40-costtcga}{0.060}
+\herset{d13-costyale}{0.070}
+\herset{d13-costtcga}{0.045}
+\herset{d14-costyale}{0.091}
+\herset{d14-costtcga}{0.095}
+\herset{d15rand-costyale}{0.141}
+\herset{d15rand-costtcga}{0.019}
+\herset{d15s50k-costyale}{0.047}
+\herset{d15s50k-costtcga}{0.011}
+\herset{d15final-costyale}{0.141}
+\herset{d15final-costtcga}{0.035}
+\herset{d16rand-costyale}{0.053}
+\herset{d16rand-costtcga}{0.029}
+\herset{d16s50k-costyale}{0.075}
+\herset{d16s50k-costtcga}{0.015}
+\herset{d16final-costyale}{0.075}
+\herset{d16final-costtcga}{0.030}
+\herset{d18s40k-costyale}{0.042}
+\herset{d18s40k-costtcga}{-0.007}
+\herset{d18s45k-costyale}{0.014}
+\herset{d18s45k-costtcga}{0.011}
+\herset{d18s50k-costyale}{0.049}
+\herset{d18s50k-costtcga}{0.001}
+\herset{hibou-costyale}{0.062}
+\herset{hibou-costtcga}{0.013}
+\herset{phikon-costyale}{0.063}
+\herset{phikon-costtcga}{0.008}
+\herset{virchow2-costyale}{0.125}
+\herset{virchow2-costtcga}{-0.020}
+\herset{midnight-costyale}{0.090}
+\herset{midnight-costtcga}{-0.027}
+\herset{midnightcm-costyale}{0.078}
+\herset{midnightcm-costtcga}{-0.027}
+\herset{midnight1um-costyale}{0.077}
+\herset{midnight1um-costtcga}{-0.023}
+\herset{hoptaug-costyale}{0.100}
+\herset{hoptaug-costtcga}{0.012}
+\herset{hoptrep-costyale}{0.108}
+\herset{hoptrep-costtcga}{0.012}
+\herset{hoptcard-costyale}{0.077}
+\herset{hoptcard-costtcga}{0.022}
+\herset{hoptcm-costyale}{0.090}
+\herset{hoptcm-costtcga}{0.009}
+\herset{hopt1um-costyale}{0.106}
+\herset{hopt1um-costtcga}{0.017}
+\herset{ta-budget-yale-sslvits16final400tiles}{0.827}
+\herset{ta-budget-yale-sslvits16final400tiles-sd}{0.048}
+\herset{ta-budget-yale-sslvits16final400tiles-rank}{1}
+\herset{ta-budget-yale-p9finalall}{0.811}
+\herset{ta-budget-yale-p9finalall-sd}{0.026}
+\herset{ta-budget-yale-p9finalall-rank}{2}
+\herset{ta-budget-yale-p9finalall-pbest}{0.097}
+\herset{ta-budget-yale-n}{2}
+\herset{ta-budget-yale-ntop}{2}
+\herset{ta-eq-ish-hoptcard}{0.765}
+\herset{ta-eq-ish-hoptcard-sd}{0.107}
+\herset{ta-eq-ish-hoptcard-rank}{1}
+\herset{ta-eq-ish-s16final}{0.698}
+\herset{ta-eq-ish-s16final-sd}{0.072}
+\herset{ta-eq-ish-s16final-rank}{2}
+\herset{ta-eq-ish-s16final-pbest}{0.080}
+\herset{ta-eq-ish-d13}{0.690}
+\herset{ta-eq-ish-d13-sd}{0.103}
+\herset{ta-eq-ish-d13-rank}{3}
+\herset{ta-eq-ish-d13-pbest}{0.007}
+\herset{ta-eq-ish-midnight}{0.675}
+\herset{ta-eq-ish-midnight-sd}{0.119}
+\herset{ta-eq-ish-midnight-rank}{4}
+\herset{ta-eq-ish-midnight-pbest}{0.008}
+\herset{ta-eq-ish-n}{4}
+\herset{ta-eq-ish-ntop}{2}
+\herset{ta-her2-tcga-hoptcm}{0.669}
+\herset{ta-her2-tcga-hoptcm-sd}{0.072}
+\herset{ta-her2-tcga-hoptcm-rank}{1}
+\herset{ta-her2-tcga-hoptcm-prn}{0.001}
+\herset{ta-her2-tcga-hopt1um}{0.665}
+\herset{ta-her2-tcga-hopt1um-sd}{0.060}
+\herset{ta-her2-tcga-hopt1um-rank}{2}
+\herset{ta-her2-tcga-hopt1um-pbest}{0.376}
+\herset{ta-her2-tcga-hopt1um-prn}{0.002}
+\herset{ta-her2-tcga-d18s40k}{0.663}
+\herset{ta-her2-tcga-d18s40k-sd}{0.062}
+\herset{ta-her2-tcga-d18s40k-rank}{3}
+\herset{ta-her2-tcga-d18s40k-pbest}{0.312}
+\herset{ta-her2-tcga-d18s40k-prn}{0.001}
+\herset{ta-her2-tcga-hoptcard}{0.660}
+\herset{ta-her2-tcga-hoptcard-sd}{0.072}
+\herset{ta-her2-tcga-hoptcard-rank}{4}
+\herset{ta-her2-tcga-hoptcard-pbest}{0.278}
+\herset{ta-her2-tcga-hoptcard-prn}{0.001}
+\herset{ta-her2-tcga-hoptaug}{0.659}
+\herset{ta-her2-tcga-hoptaug-sd}{0.058}
+\herset{ta-her2-tcga-hoptaug-rank}{5}
+\herset{ta-her2-tcga-hoptaug-pbest}{0.097}
+\herset{ta-her2-tcga-hoptaug-prn}{0.001}
+\herset{ta-her2-tcga-d16final}{0.658}
+\herset{ta-her2-tcga-d16final-sd}{0.073}
+\herset{ta-her2-tcga-d16final-rank}{6}
+\herset{ta-her2-tcga-d16final-pbest}{0.312}
+\herset{ta-her2-tcga-d16final-prn}{0.002}
+\herset{ta-her2-tcga-phikon}{0.656}
+\herset{ta-her2-tcga-phikon-sd}{0.058}
+\herset{ta-her2-tcga-phikon-rank}{7}
+\herset{ta-her2-tcga-phikon-pbest}{0.216}
+\herset{ta-her2-tcga-phikon-prn}{0.007}
+\herset{ta-her2-tcga-d14}{0.653}
+\herset{ta-her2-tcga-d14-sd}{0.082}
+\herset{ta-her2-tcga-d14-rank}{8}
+\herset{ta-her2-tcga-d14-pbest}{0.188}
+\herset{ta-her2-tcga-d14-prn}{0.003}
+\herset{ta-her2-tcga-d16s50k}{0.651}
+\herset{ta-her2-tcga-d16s50k-sd}{0.050}
+\herset{ta-her2-tcga-d16s50k-rank}{9}
+\herset{ta-her2-tcga-d16s50k-pbest}{0.138}
+\herset{ta-her2-tcga-d16s50k-prn}{0.005}
+\herset{ta-her2-tcga-d18s45k}{0.645}
+\herset{ta-her2-tcga-d18s45k-sd}{0.064}
+\herset{ta-her2-tcga-d18s45k-rank}{10}
+\herset{ta-her2-tcga-d18s45k-pbest}{0.007}
+\herset{ta-her2-tcga-d18s45k-prn}{0.001}
+\herset{ta-her2-tcga-midnightcm}{0.630}
+\herset{ta-her2-tcga-midnightcm-sd}{0.048}
+\herset{ta-her2-tcga-midnightcm-rank}{11}
+\herset{ta-her2-tcga-midnightcm-pbest}{0.053}
+\herset{ta-her2-tcga-midnightcm-prn}{0.019}
+\herset{ta-her2-tcga-b16s40}{0.621}
+\herset{ta-her2-tcga-b16s40-sd}{0.069}
+\herset{ta-her2-tcga-b16s40-rank}{12}
+\herset{ta-her2-tcga-b16s40-pbest}{0.053}
+\herset{ta-her2-tcga-b16s40-prn}{0.065}
+\herset{ta-her2-tcga-d13}{0.616}
+\herset{ta-her2-tcga-d13-sd}{0.040}
+\herset{ta-her2-tcga-d13-rank}{13}
+\herset{ta-her2-tcga-d13-pbest}{0.010}
+\herset{ta-her2-tcga-d13-prn}{0.080}
+\herset{ta-her2-tcga-s16s45}{0.612}
+\herset{ta-her2-tcga-s16s45-sd}{0.066}
+\herset{ta-her2-tcga-s16s45-rank}{14}
+\herset{ta-her2-tcga-s16s45-pbest}{0.005}
+\herset{ta-her2-tcga-s16s45-prn}{0.080}
+\herset{ta-her2-tcga-s16s35}{0.610}
+\herset{ta-her2-tcga-s16s35-sd}{0.056}
+\herset{ta-her2-tcga-s16s35-rank}{15}
+\herset{ta-her2-tcga-s16s35-pbest}{0.019}
+\herset{ta-her2-tcga-s16s35-prn}{0.042}
+\herset{ta-her2-tcga-midnight}{0.606}
+\herset{ta-her2-tcga-midnight-sd}{0.034}
+\herset{ta-her2-tcga-midnight-rank}{16}
+\herset{ta-her2-tcga-midnight-pbest}{0.007}
+\herset{ta-her2-tcga-midnight-prn}{0.080}
+\herset{ta-her2-tcga-hibou}{0.605}
+\herset{ta-her2-tcga-hibou-sd}{0.048}
+\herset{ta-her2-tcga-hibou-rank}{17}
+\herset{ta-her2-tcga-hibou-pbest}{0.005}
+\herset{ta-her2-tcga-hibou-prn}{0.080}
+\herset{ta-her2-tcga-midnight1um}{0.603}
+\herset{ta-her2-tcga-midnight1um-sd}{0.054}
+\herset{ta-her2-tcga-midnight1um-rank}{18}
+\herset{ta-her2-tcga-midnight1um-pbest}{0.003}
+\herset{ta-her2-tcga-midnight1um-prn}{0.216}
+\herset{ta-her2-tcga-s16s40}{0.597}
+\herset{ta-her2-tcga-s16s40-sd}{0.070}
+\herset{ta-her2-tcga-s16s40-rank}{19}
+\herset{ta-her2-tcga-s16s40-pbest}{0.014}
+\herset{ta-her2-tcga-s16s40-prn}{0.188}
+\herset{ta-her2-tcga-s16final}{0.595}
+\herset{ta-her2-tcga-s16final-sd}{0.052}
+\herset{ta-her2-tcga-s16final-rank}{20}
+\herset{ta-her2-tcga-s16final-pbest}{0.010}
+\herset{ta-her2-tcga-s16final-prn}{0.188}
+\herset{ta-her2-tcga-s16s65}{0.594}
+\herset{ta-her2-tcga-s16s65-sd}{0.061}
+\herset{ta-her2-tcga-s16s65-rank}{21}
+\herset{ta-her2-tcga-s16s65-pbest}{0.010}
+\herset{ta-her2-tcga-s16s65-prn}{0.246}
+\herset{ta-her2-tcga-s16s60}{0.592}
+\herset{ta-her2-tcga-s16s60-sd}{0.049}
+\herset{ta-her2-tcga-s16s60-rank}{22}
+\herset{ta-her2-tcga-s16s60-pbest}{0.010}
+\herset{ta-her2-tcga-s16s60-prn}{0.188}
+\herset{ta-her2-tcga-b16s10}{0.591}
+\herset{ta-her2-tcga-b16s10-sd}{0.050}
+\herset{ta-her2-tcga-b16s10-rank}{23}
+\herset{ta-her2-tcga-b16s10-pbest}{0.003}
+\herset{ta-her2-tcga-b16s10-prn}{0.138}
+\herset{ta-her2-tcga-s16s20}{0.591}
+\herset{ta-her2-tcga-s16s20-sd}{0.047}
+\herset{ta-her2-tcga-s16s20-rank}{24}
+\herset{ta-her2-tcga-s16s20-pbest}{0.007}
+\herset{ta-her2-tcga-s16s20-prn}{0.188}
+\herset{ta-her2-tcga-d16rand}{0.590}
+\herset{ta-her2-tcga-d16rand-sd}{0.079}
+\herset{ta-her2-tcga-d16rand-rank}{25}
+\herset{ta-her2-tcga-d16rand-pbest}{0.001}
+\herset{ta-her2-tcga-d16rand-prn}{0.246}
+\herset{ta-her2-tcga-b16s35}{0.590}
+\herset{ta-her2-tcga-b16s35-sd}{0.061}
+\herset{ta-her2-tcga-b16s35-rank}{26}
+\herset{ta-her2-tcga-b16s35-pbest}{0.002}
+\herset{ta-her2-tcga-b16s35-prn}{0.216}
+\herset{ta-her2-tcga-s16s55}{0.589}
+\herset{ta-her2-tcga-s16s55-sd}{0.036}
+\herset{ta-her2-tcga-s16s55-rank}{27}
+\herset{ta-her2-tcga-s16s55-pbest}{0.005}
+\herset{ta-her2-tcga-s16s55-prn}{0.138}
+\herset{ta-her2-tcga-s16s50}{0.587}
+\herset{ta-her2-tcga-s16s50-sd}{0.049}
+\herset{ta-her2-tcga-s16s50-rank}{28}
+\herset{ta-her2-tcga-s16s50-pbest}{0.002}
+\herset{ta-her2-tcga-s16s50-prn}{0.216}
+\herset{ta-her2-tcga-d15s50k}{0.572}
+\herset{ta-her2-tcga-d15s50k-sd}{0.043}
+\herset{ta-her2-tcga-d15s50k-rank}{29}
+\herset{ta-her2-tcga-d15s50k-pbest}{0.002}
+\herset{ta-her2-tcga-d15s50k-prn}{0.278}
+\herset{ta-her2-tcga-rn50}{0.567}
+\herset{ta-her2-tcga-rn50-sd}{0.068}
+\herset{ta-her2-tcga-rn50-rank}{30}
+\herset{ta-her2-tcga-rn50-pbest}{0.001}
+\herset{ta-her2-tcga-d15final}{0.560}
+\herset{ta-her2-tcga-d15final-sd}{0.097}
+\herset{ta-her2-tcga-d15final-rank}{31}
+\herset{ta-her2-tcga-d15final-pbest}{0.007}
+\herset{ta-her2-tcga-d15final-prn}{0.577}
+\herset{ta-her2-tcga-d15rand}{0.558}
+\herset{ta-her2-tcga-d15rand-sd}{0.051}
+\herset{ta-her2-tcga-d15rand-rank}{32}
+\herset{ta-her2-tcga-d15rand-pbest}{0.001}
+\herset{ta-her2-tcga-d15rand-prn}{0.652}
+\herset{ta-her2-tcga-s16s5}{0.543}
+\herset{ta-her2-tcga-s16s5-sd}{0.055}
+\herset{ta-her2-tcga-s16s5-rank}{33}
+\herset{ta-her2-tcga-s16s5-pbest}{0.002}
+\herset{ta-her2-tcga-s16s5-prn}{0.722}
+\herset{ta-her2-tcga-dinov2s}{0.541}
+\herset{ta-her2-tcga-dinov2s-sd}{0.081}
+\herset{ta-her2-tcga-dinov2s-rank}{34}
+\herset{ta-her2-tcga-dinov2s-pbest}{0.003}
+\herset{ta-her2-tcga-dinov2s-prn}{0.688}
+\herset{ta-her2-tcga-rand}{0.485}
+\herset{ta-her2-tcga-rand-sd}{0.054}
+\herset{ta-her2-tcga-rand-rank}{35}
+\herset{ta-her2-tcga-rand-pbest}{0.001}
+\herset{ta-her2-tcga-rand-prn}{0.995}
+\herset{ta-her2-tcga-n}{35}
+\herset{ta-her2-tcga-p-phikon-d18s40k}{0.539}
+\herset{ta-her2-tcga-d-phikon-d18s40k}{-0.006}
+\herset{ta-her2-tcga-p-phikon-d16final}{0.539}
+\herset{ta-her2-tcga-d-phikon-d16final}{-0.001}
+\herset{ta-her2-tcga-ntop}{11}
+\herset{ta-her2-tcga-bestrank}{6}
+\herset{ta-her2-tcga-bestgap}{0.002}
+\herset{ta-her2-yale-hopt1um}{0.913}
+\herset{ta-her2-yale-hopt1um-sd}{0.067}
+\herset{ta-her2-yale-hopt1um-rank}{1}
+\herset{ta-her2-yale-hopt1um-prn}{0.001}
+\herset{ta-her2-yale-hoptcard}{0.913}
+\herset{ta-her2-yale-hoptcard-sd}{0.061}
+\herset{ta-her2-yale-hoptcard-rank}{2}
+\herset{ta-her2-yale-hoptcard-pbest}{0.451}
+\herset{ta-her2-yale-hoptcard-prn}{0.001}
+\herset{ta-her2-yale-hoptcm}{0.911}
+\herset{ta-her2-yale-hoptcm-sd}{0.057}
+\herset{ta-her2-yale-hoptcm-rank}{3}
+\herset{ta-her2-yale-hoptcm-pbest}{0.161}
+\herset{ta-her2-yale-hoptcm-prn}{0.001}
+\herset{ta-her2-yale-hoptaug}{0.907}
+\herset{ta-her2-yale-hoptaug-sd}{0.058}
+\herset{ta-her2-yale-hoptaug-rank}{4}
+\herset{ta-her2-yale-hoptaug-pbest}{0.152}
+\herset{ta-her2-yale-hoptaug-prn}{0.001}
+\herset{ta-her2-yale-phikon}{0.901}
+\herset{ta-her2-yale-phikon-sd}{0.059}
+\herset{ta-her2-yale-phikon-rank}{5}
+\herset{ta-her2-yale-phikon-pbest}{0.116}
+\herset{ta-her2-yale-phikon-prn}{0.001}
+\herset{ta-her2-yale-midnightcm}{0.878}
+\herset{ta-her2-yale-midnightcm-sd}{0.064}
+\herset{ta-her2-yale-midnightcm-rank}{6}
+\herset{ta-her2-yale-midnightcm-pbest}{0.013}
+\herset{ta-her2-yale-midnightcm-prn}{0.001}
+\herset{ta-her2-yale-d16final}{0.870}
+\herset{ta-her2-yale-d16final-sd}{0.053}
+\herset{ta-her2-yale-d16final-rank}{7}
+\herset{ta-her2-yale-d16final-pbest}{0.032}
+\herset{ta-her2-yale-d16final-prn}{0.001}
+\herset{ta-her2-yale-d18s40k}{0.856}
+\herset{ta-her2-yale-d18s40k-sd}{0.052}
+\herset{ta-her2-yale-d18s40k-rank}{8}
+\herset{ta-her2-yale-d18s40k-pbest}{0.005}
+\herset{ta-her2-yale-d18s40k-prn}{0.003}
+\herset{ta-her2-yale-d14}{0.856}
+\herset{ta-her2-yale-d14-sd}{0.046}
+\herset{ta-her2-yale-d14-rank}{9}
+\herset{ta-her2-yale-d14-pbest}{0.002}
+\herset{ta-her2-yale-d14-prn}{0.001}
+\herset{ta-her2-yale-midnight}{0.856}
+\herset{ta-her2-yale-midnight-sd}{0.043}
+\herset{ta-her2-yale-midnight-rank}{10}
+\herset{ta-her2-yale-midnight-pbest}{0.007}
+\herset{ta-her2-yale-midnight-prn}{0.001}
+\herset{ta-her2-yale-hibou}{0.853}
+\herset{ta-her2-yale-hibou-sd}{0.062}
+\herset{ta-her2-yale-hibou-rank}{11}
+\herset{ta-her2-yale-hibou-pbest}{0.001}
+\herset{ta-her2-yale-hibou-prn}{0.002}
+\herset{ta-her2-yale-d18s50k}{0.851}
+\herset{ta-her2-yale-d18s50k-sd}{0.046}
+\herset{ta-her2-yale-d18s50k-rank}{12}
+\herset{ta-her2-yale-d18s50k-pbest}{0.007}
+\herset{ta-her2-yale-d18s50k-prn}{0.003}
+\herset{ta-her2-yale-b16s35}{0.847}
+\herset{ta-her2-yale-b16s35-sd}{0.047}
+\herset{ta-her2-yale-b16s35-rank}{13}
+\herset{ta-her2-yale-b16s35-pbest}{0.002}
+\herset{ta-her2-yale-b16s35-prn}{0.001}
+\herset{ta-her2-yale-d16s50k}{0.846}
+\herset{ta-her2-yale-d16s50k-sd}{0.052}
+\herset{ta-her2-yale-d16s50k-rank}{14}
+\herset{ta-her2-yale-d16s50k-pbest}{0.004}
+\herset{ta-her2-yale-d16s50k-prn}{0.001}
+\herset{ta-her2-yale-s16s55}{0.845}
+\herset{ta-her2-yale-s16s55-sd}{0.057}
+\herset{ta-her2-yale-s16s55-rank}{15}
+\herset{ta-her2-yale-s16s55-pbest}{0.005}
+\herset{ta-her2-yale-s16s55-prn}{0.001}
+\herset{ta-her2-yale-midnight1um}{0.840}
+\herset{ta-her2-yale-midnight1um-sd}{0.063}
+\herset{ta-her2-yale-midnight1um-rank}{16}
+\herset{ta-her2-yale-midnight1um-pbest}{0.002}
+\herset{ta-her2-yale-midnight1um-prn}{0.002}
+\herset{ta-her2-yale-b16s40}{0.840}
+\herset{ta-her2-yale-b16s40-sd}{0.060}
+\herset{ta-her2-yale-b16s40-rank}{17}
+\herset{ta-her2-yale-b16s40-pbest}{0.003}
+\herset{ta-her2-yale-b16s40-prn}{0.001}
+\herset{ta-her2-yale-d18s45k}{0.839}
+\herset{ta-her2-yale-d18s45k-sd}{0.049}
+\herset{ta-her2-yale-d18s45k-rank}{18}
+\herset{ta-her2-yale-d18s45k-pbest}{0.002}
+\herset{ta-her2-yale-d18s45k-prn}{0.005}
+\herset{ta-her2-yale-d13}{0.838}
+\herset{ta-her2-yale-d13-sd}{0.066}
+\herset{ta-her2-yale-d13-rank}{19}
+\herset{ta-her2-yale-d13-pbest}{0.005}
+\herset{ta-her2-yale-d13-prn}{0.002}
+\herset{ta-her2-yale-s16s35}{0.831}
+\herset{ta-her2-yale-s16s35-sd}{0.058}
+\herset{ta-her2-yale-s16s35-rank}{20}
+\herset{ta-her2-yale-s16s35-pbest}{0.001}
+\herset{ta-her2-yale-s16s35-prn}{0.001}
+\herset{ta-her2-yale-s16s50}{0.831}
+\herset{ta-her2-yale-s16s50-sd}{0.071}
+\herset{ta-her2-yale-s16s50-rank}{21}
+\herset{ta-her2-yale-s16s50-pbest}{0.003}
+\herset{ta-her2-yale-s16s50-prn}{0.001}
+\herset{ta-her2-yale-s16s40}{0.830}
+\herset{ta-her2-yale-s16s40-sd}{0.070}
+\herset{ta-her2-yale-s16s40-rank}{22}
+\herset{ta-her2-yale-s16s40-pbest}{0.010}
+\herset{ta-her2-yale-s16s40-prn}{0.010}
+\herset{ta-her2-yale-d16rand}{0.828}
+\herset{ta-her2-yale-d16rand-sd}{0.077}
+\herset{ta-her2-yale-d16rand-rank}{23}
+\herset{ta-her2-yale-d16rand-pbest}{0.003}
+\herset{ta-her2-yale-d16rand-prn}{0.002}
+\herset{ta-her2-yale-s16s60}{0.827}
+\herset{ta-her2-yale-s16s60-sd}{0.047}
+\herset{ta-her2-yale-s16s60-rank}{24}
+\herset{ta-her2-yale-s16s60-pbest}{0.003}
+\herset{ta-her2-yale-s16s60-prn}{0.001}
+\herset{ta-her2-yale-s16final}{0.827}
+\herset{ta-her2-yale-s16final-sd}{0.048}
+\herset{ta-her2-yale-s16final-rank}{25}
+\herset{ta-her2-yale-s16final-pbest}{0.001}
+\herset{ta-her2-yale-s16final-prn}{0.001}
+\herset{ta-her2-yale-b16s45}{0.820}
+\herset{ta-her2-yale-b16s45-sd}{0.047}
+\herset{ta-her2-yale-b16s45-rank}{26}
+\herset{ta-her2-yale-b16s45-pbest}{0.001}
+\herset{ta-her2-yale-b16s45-prn}{0.001}
+\herset{ta-her2-yale-s16s45}{0.817}
+\herset{ta-her2-yale-s16s45-sd}{0.058}
+\herset{ta-her2-yale-s16s45-rank}{27}
+\herset{ta-her2-yale-s16s45-pbest}{0.001}
+\herset{ta-her2-yale-s16s45-prn}{0.001}
+\herset{ta-her2-yale-b16s20}{0.813}
+\herset{ta-her2-yale-b16s20-sd}{0.051}
+\herset{ta-her2-yale-b16s20-rank}{28}
+\herset{ta-her2-yale-b16s20-pbest}{0.001}
+\herset{ta-her2-yale-b16s20-prn}{0.002}
+\herset{ta-her2-yale-d15final}{0.809}
+\herset{ta-her2-yale-d15final-sd}{0.059}
+\herset{ta-her2-yale-d15final-rank}{29}
+\herset{ta-her2-yale-d15final-pbest}{0.002}
+\herset{ta-her2-yale-d15final-prn}{0.002}
+\herset{ta-her2-yale-d15s50k}{0.808}
+\herset{ta-her2-yale-d15s50k-sd}{0.044}
+\herset{ta-her2-yale-d15s50k-rank}{30}
+\herset{ta-her2-yale-d15s50k-pbest}{0.002}
+\herset{ta-her2-yale-d15s50k-prn}{0.005}
+\herset{ta-her2-yale-d15rand}{0.807}
+\herset{ta-her2-yale-d15rand-sd}{0.057}
+\herset{ta-her2-yale-d15rand-rank}{31}
+\herset{ta-her2-yale-d15rand-pbest}{0.001}
+\herset{ta-her2-yale-d15rand-prn}{0.010}
+\herset{ta-her2-yale-s16s65}{0.802}
+\herset{ta-her2-yale-s16s65-sd}{0.045}
+\herset{ta-her2-yale-s16s65-rank}{32}
+\herset{ta-her2-yale-s16s65-pbest}{0.002}
+\herset{ta-her2-yale-s16s65-prn}{0.001}
+\herset{ta-her2-yale-s16s20}{0.799}
+\herset{ta-her2-yale-s16s20-sd}{0.073}
+\herset{ta-her2-yale-s16s20-rank}{33}
+\herset{ta-her2-yale-s16s20-pbest}{0.001}
+\herset{ta-her2-yale-s16s20-prn}{0.004}
+\herset{ta-her2-yale-dinov2s}{0.794}
+\herset{ta-her2-yale-dinov2s-sd}{0.057}
+\herset{ta-her2-yale-dinov2s-rank}{34}
+\herset{ta-her2-yale-dinov2s-pbest}{0.001}
+\herset{ta-her2-yale-dinov2s-prn}{0.024}
+\herset{ta-her2-yale-s16s5}{0.786}
+\herset{ta-her2-yale-s16s5-sd}{0.083}
+\herset{ta-her2-yale-s16s5-rank}{35}
+\herset{ta-her2-yale-s16s5-pbest}{0.001}
+\herset{ta-her2-yale-s16s5-prn}{0.032}
+\herset{ta-her2-yale-b16s10}{0.756}
+\herset{ta-her2-yale-b16s10-sd}{0.054}
+\herset{ta-her2-yale-b16s10-rank}{36}
+\herset{ta-her2-yale-b16s10-pbest}{0.001}
+\herset{ta-her2-yale-b16s10-prn}{0.097}
+\herset{ta-her2-yale-rn50}{0.730}
+\herset{ta-her2-yale-rn50-sd}{0.073}
+\herset{ta-her2-yale-rn50-rank}{37}
+\herset{ta-her2-yale-rn50-pbest}{0.001}
+\herset{ta-her2-yale-rand}{0.661}
+\herset{ta-her2-yale-rand-sd}{0.065}
+\herset{ta-her2-yale-rand-rank}{38}
+\herset{ta-her2-yale-rand-pbest}{0.001}
+\herset{ta-her2-yale-rand-prn}{0.995}
+\herset{ta-her2-yale-n}{38}
+\herset{ta-her2-yale-p-phikon-d18s40k}{0.014}
+\herset{ta-her2-yale-d-phikon-d18s40k}{+0.045}
+\herset{ta-her2-yale-p-phikon-d16final}{0.080}
+\herset{ta-her2-yale-d-phikon-d16final}{+0.031}
+\herset{ta-her2-yale-ntop}{5}
+\herset{ta-her2-yale-bestrank}{7}
+\herset{ta-her2-yale-bestgap}{0.043}
+\herset{agree-yale}{0.93}
+\herset{agree-yale-n}{36}
+\herset{agree-tcga}{0.87}
+\herset{agree-tcga-n}{35}
+\herset{seedsd-median}{0.012}
+\herset{seedsd-max}{0.066}
+\herset{seedsd-n}{152}
+\herset{ta-her2-tcga-check}{matches}
+\herset{ta-her2-yale-check}{matches}
+\herset{ov-cohort-slides}{490}
+\herset{ov-cohort-cases}{490}
+\herset{ov-cohort-positive}{156}
+\herset{ov-corpus-selected-slides}{9,752}
+\herset{ov-corpus-selected-brca}{1,062}
+\herset{ov-corpus-programmes}{TCGA}
+\herset{ov-overlap-slides}{490}
+\herset{ov-overlap-cases}{490}
+\herset{ov-overlap-positive-slides}{156}
diff --git a/data/paper/book-data/her2_matrix.dat b/data/paper/book-data/her2_matrix.dat
new file mode 100644
index 0000000000000000000000000000000000000000..8638c39ed746ab652fa40cf16d4d16db20ea94e6
--- /dev/null
+++ b/data/paper/book-data/her2_matrix.dat
@@ -0,0 +1,39 @@
+key group probe yy yy_sd tt tt_sd ty ty_sd yt yt_sd yy_mf yy_mf_sd tt_mf tt_mf_sd ty_mf ty_mf_sd yt_mf yt_mf_sd
+vits16-random-init ctl 0.0962 0.5900 0.0040 0.5080 0.0124 0.4375 0.0317 0.4849 0.0020 0.6021 0.0104 0.5166 0.0137 0.4466 0.0333 0.4892 0.0020
+dinov2-vits14 ctl 0.3104 0.7192 0.0203 0.5011 0.0226 0.5889 0.0203 0.5764 0.0115 0.7333 0.0085 0.5249 0.0178 0.5615 0.0139 0.5629 0.0065
+resnet50-imagenet ctl nan 0.7009 0.0427 0.5517 0.0279 0.6296 0.0149 0.5606 0.0069 0.7204 0.0603 0.5541 0.0277 0.6048 0.0082 0.5400 0.0031
+ssl-vits16-step5k s16 0.1954 0.7264 0.0064 0.5697 0.0129 0.6764 0.0184 0.5228 0.0041 0.7500 0.0056 0.5762 0.0147 0.6074 0.0217 0.5122 0.0058
+ssl-vits16-step20k s16 0.2883 0.7382 0.0126 0.5675 0.0106 0.6174 0.0430 0.5451 0.0010 0.7838 0.0231 0.5838 0.0086 0.5928 0.0179 0.5393 0.0016
+ssl-vits16-step35k s16 0.3337 0.8039 0.0106 0.5992 0.0082 0.5613 0.0149 0.5306 0.0038 0.8191 0.0050 0.6349 0.0055 0.5750 0.0144 0.5265 0.0048
+ssl-vits16-step40k s16 0.3363 0.7919 0.0193 0.6199 0.0090 0.5651 0.0174 0.5144 0.0034 0.8122 0.0083 0.6318 0.0188 0.5590 0.0152 0.5137 0.0028
+ssl-vits16-step45k s16 0.3368 0.7601 0.0245 0.5887 0.0192 0.5873 0.0660 0.5253 0.0061 0.7785 0.0237 0.5968 0.0295 0.5824 0.0382 0.5262 0.0033
+ssl-vits16-step50k s16 0.3379 0.7808 0.0174 0.6085 0.0042 0.5499 0.0167 0.5281 0.0060 0.8165 0.0120 0.6248 0.0064 0.5506 0.0041 0.5204 0.0063
+ssl-vits16-step55k s16 0.3422 0.7880 0.0285 0.5888 0.0031 0.6004 0.0520 0.5322 0.0064 0.8096 0.0256 0.6164 0.0160 0.5806 0.0272 0.5236 0.0040
+ssl-vits16-step60k s16 0.3412 0.7775 0.0090 0.5958 0.0062 0.5922 0.0314 0.5318 0.0034 0.8186 0.0078 0.6057 0.0076 0.5742 0.0079 0.5262 0.0047
+ssl-vits16-step65k s16 0.3448 0.7588 0.0154 0.6211 0.0165 0.5530 0.0541 0.5177 0.0018 0.8045 0.0095 0.6338 0.0107 0.5392 0.0256 0.5188 0.0055
+ssl-vits16-final s16 0.3415 0.7675 0.0267 0.5891 0.0128 0.5841 0.0385 0.5205 0.0029 0.8204 0.0206 0.6085 0.0177 0.5872 0.0372 0.5155 0.0030
+ssl-vitb16-step10k b16 0.2753 0.7575 0.0358 0.5877 0.0045 0.5724 0.0202 0.5307 0.0105 0.7751 0.0165 0.6038 0.0033 0.5575 0.0028 0.5275 0.0085
+ssl-vitb16-step35k b16 0.3636 0.8290 0.0099 0.5819 0.0184 0.6380 0.0233 0.5455 0.0017 0.8518 0.0021 0.6152 0.0111 0.5975 0.0230 0.5364 0.0029
+ssl-vitb16-step40k b16 0.3834 0.7934 0.0135 0.6074 0.0161 0.6651 0.0377 0.5472 0.0057 0.8103 0.0140 0.6306 0.0085 0.6187 0.0115 0.5319 0.0027
+distill-midnight-20k dst 0.4872 0.7714 0.0195 0.5899 0.0170 0.7010 0.0206 0.5454 0.0081 0.8063 0.0047 0.5863 0.0236 0.6666 0.0171 0.5403 0.0053
+distill-hoptimus-20k dst 0.4891 0.8064 0.0183 0.6688 0.0101 0.7157 0.0151 0.5733 0.0040 0.8438 0.0134 0.6764 0.0125 0.6989 0.0201 0.5672 0.0056
+distill-midnight-5k-from-random dst 0.2887 0.7616 0.0241 0.5474 0.0242 0.6205 0.0347 0.5288 0.0026 0.7686 0.0230 0.5606 0.0207 0.5987 0.0349 0.5241 0.0050
+distill-midnight-5k-from-vits16-step50k dst 0.4502 0.7718 0.0161 0.5688 0.0093 0.7244 0.0175 0.5576 0.0113 0.8077 0.0152 0.5826 0.0130 0.6784 0.0132 0.5555 0.0049
+distill-midnight-5k-from-vits16-final dst 0.4605 0.7923 0.0214 0.5878 0.0072 0.6512 0.0293 0.5526 0.0017 0.8221 0.0128 0.6054 0.0199 0.6192 0.0122 0.5426 0.0035
+distill-hoptimus-5k-from-random dst 0.3291 0.7922 0.0088 0.5922 0.0143 0.7387 0.0281 0.5632 0.0016 0.8157 0.0167 0.6197 0.0025 0.6986 0.0170 0.5618 0.0011
+distill-hoptimus-5k-from-vits16-step50k dst 0.4459 0.8143 0.0083 0.6361 0.0038 0.7393 0.0173 0.6215 0.0038 0.8295 0.0044 0.6721 0.0082 0.7225 0.0131 0.5962 0.0058
+distill-hoptimus-5k-from-vits16-final dst 0.4514 0.8196 0.0197 0.6228 0.0229 0.7443 0.0219 0.5930 0.0059 0.8425 0.0088 0.6577 0.0155 0.7283 0.0126 0.5774 0.0025
+distill-hoptimus-vitb16-5k-from-vitb16-step40k dstb 0.4714 0.8177 0.0098 0.6332 0.0157 0.7757 0.0098 0.6406 0.0080 0.8471 0.0013 0.6621 0.0065 0.7585 0.0118 0.6224 0.0025
+distill-hoptimus-vitb16-5k-from-vitb16-step45k dstb 0.4797 0.7765 0.0094 0.6329 0.0143 0.7623 0.0040 0.6215 0.0069 0.8289 0.0128 0.6675 0.0138 0.7474 0.0091 0.6038 0.0048
+distill-hoptimus-vitb16-5k-from-vitb16-step50k dstb 0.4899 0.8032 0.0397 0.6148 0.0022 0.7547 0.0075 0.6136 0.0012 0.8414 0.0124 0.6414 0.0020 0.7293 0.0107 0.6005 0.0038
+hibou-b ref nan 0.7794 0.0119 0.6442 0.0161 0.7172 0.0299 0.6309 0.0105 0.8167 0.0151 0.6643 0.0093 0.6791 0.0243 0.6057 0.0049
+phikon ref 0.5507 0.8756 0.0047 0.6690 0.0085 0.8128 0.0191 0.6606 0.0003 0.8902 0.0064 0.6872 0.0065 0.7682 0.0168 0.6484 0.0030
+virchow2 ref nan 0.8673 0.0086 0.6336 0.0146 0.7425 0.0349 0.6541 0.0058 0.8889 0.0095 0.6652 0.0192 0.7082 0.0240 0.6356 0.0092
+midnight12k ref 0.6904 0.8064 0.0091 0.5970 0.0028 0.7163 0.0086 0.6240 0.0039 0.8340 0.0028 0.6111 0.0049 0.6954 0.0027 0.6059 0.0020
+midnight12k-cls-mean ref 0.7050 0.8114 0.0112 0.6105 0.0084 0.7337 0.0209 0.6376 0.0039 0.8533 0.0071 0.6324 0.0042 0.6998 0.0115 0.6271 0.0043
+midnight12k-1.0um ref nan 0.8013 0.0060 0.5858 0.0127 0.7243 0.0269 0.6087 0.0054 0.8253 0.0222 0.5984 0.0129 0.6807 0.0189 0.6007 0.0034
+hoptimus0-imagenetnorm ref nan 0.8978 0.0104 0.6685 0.0232 0.7977 0.0172 0.6562 0.0013 0.9119 0.0064 0.6828 0.0155 0.7465 0.0191 0.6420 0.0020
+hoptimus0-imagenetnorm-repeat ref nan 0.8954 0.0143 0.6683 0.0145 0.7871 0.0310 0.6562 0.0052 0.9124 0.0064 0.6784 0.0145 0.7509 0.0163 0.6425 0.0028
+hoptimus0-cardnorm ref 0.5811 0.8868 0.0072 0.6755 0.0162 0.8100 0.0116 0.6531 0.0078 0.9056 0.0065 0.6957 0.0132 0.7642 0.0102 0.6419 0.0071
+hoptimus0-cls-mean ref 0.5920 0.8925 0.0157 0.6723 0.0179 0.8028 0.0253 0.6630 0.0057 0.9151 0.0018 0.6882 0.0111 0.7640 0.0115 0.6488 0.0050
+hoptimus0-1.0um ref nan 0.9088 0.0066 0.6591 0.0153 0.8033 0.0109 0.6419 0.0071 0.9247 0.0076 0.6774 0.0127 0.7640 0.0200 0.6298 0.0042
diff --git a/data/paper/book-data/her2_traj_vitb16.dat b/data/paper/book-data/her2_traj_vitb16.dat
new file mode 100644
index 0000000000000000000000000000000000000000..aab484d4bf8e4a87e8a8fdd5046bbc4ea38d699e
--- /dev/null
+++ b/data/paper/book-data/her2_traj_vitb16.dat
@@ -0,0 +1,4 @@
+step probe yy yy_sd tt tt_sd ty ty_sd yt yt_sd
+10000 0.2753 0.7575 0.0358 0.5877 0.0045 0.5724 0.0202 0.5307 0.0105
+35000 0.3636 0.8290 0.0099 0.5819 0.0184 0.6380 0.0233 0.5455 0.0017
+40000 0.3834 0.7934 0.0135 0.6074 0.0161 0.6651 0.0377 0.5472 0.0057
diff --git a/data/paper/book-data/her2_traj_vits16.dat b/data/paper/book-data/her2_traj_vits16.dat
new file mode 100644
index 0000000000000000000000000000000000000000..4a19aeaa7fcdf46ee6360d147f76cd8944b33fed
--- /dev/null
+++ b/data/paper/book-data/her2_traj_vits16.dat
@@ -0,0 +1,11 @@
+step probe yy yy_sd tt tt_sd ty ty_sd yt yt_sd
+5000 0.1954 0.7264 0.0064 0.5697 0.0129 0.6764 0.0184 0.5228 0.0041
+20000 0.2883 0.7382 0.0126 0.5675 0.0106 0.6174 0.0430 0.5451 0.0010
+35000 0.3337 0.8039 0.0106 0.5992 0.0082 0.5613 0.0149 0.5306 0.0038
+40000 0.3363 0.7919 0.0193 0.6199 0.0090 0.5651 0.0174 0.5144 0.0034
+45000 0.3368 0.7601 0.0245 0.5887 0.0192 0.5873 0.0660 0.5253 0.0061
+50000 0.3379 0.7808 0.0174 0.6085 0.0042 0.5499 0.0167 0.5281 0.0060
+55000 0.3422 0.7880 0.0285 0.5888 0.0031 0.6004 0.0520 0.5322 0.0064
+60000 0.3412 0.7775 0.0090 0.5958 0.0062 0.5922 0.0314 0.5318 0.0034
+65000 0.3448 0.7588 0.0154 0.6211 0.0165 0.5530 0.0541 0.5177 0.0018
+93338 0.3415 0.7675 0.0267 0.5891 0.0128 0.5841 0.0385 0.5205 0.0029
diff --git a/data/paper/book-data/her2_vs_trackA.dat b/data/paper/book-data/her2_vs_trackA.dat
new file mode 100644
index 0000000000000000000000000000000000000000..d5a9f5d875e36f59e18c3474c732071d979bbf6d
--- /dev/null
+++ b/data/paper/book-data/her2_vs_trackA.dat
@@ -0,0 +1,37 @@
+key group yy ta_yale tt ta_tcga
+dinov2-vits14 ctl 0.7192 0.7939 0.5011 0.5410
+distill-hoptimus-20k dst 0.8064 0.8561 0.6688 0.6532
+distill-hoptimus-5k-from-random dst 0.7922 0.8282 0.5922 0.5905
+distill-hoptimus-5k-from-vits16-final best 0.8196 0.8697 0.6228 0.6578
+distill-hoptimus-5k-from-vits16-step50k dst 0.8143 0.8455 0.6361 0.6512
+distill-hoptimus-vitb16-5k-from-vitb16-step40k dstb 0.8177 0.8563 0.6332 0.6627
+distill-hoptimus-vitb16-5k-from-vitb16-step45k dstb 0.7765 0.8395 0.6329 0.6452
+distill-hoptimus-vitb16-5k-from-vitb16-step50k dstb 0.8032 0.8513 0.6148 nan
+distill-midnight-20k dst 0.7714 0.8384 0.5899 0.6159
+distill-midnight-5k-from-random dst 0.7616 0.8068 0.5474 0.5578
+distill-midnight-5k-from-vits16-final dst 0.7923 0.8092 0.5878 0.5601
+distill-midnight-5k-from-vits16-step50k dst 0.7718 0.8079 0.5688 0.5719
+hibou-b ref 0.7794 0.8534 0.6442 0.6050
+hoptimus0-1.0um ref 0.9088 0.9134 0.6591 0.6655
+hoptimus0-cardnorm ref 0.8868 0.9129 0.6755 0.6597
+hoptimus0-cls-mean ref 0.8925 0.9105 0.6723 0.6694
+hoptimus0-imagenetnorm ref 0.8978 0.9068 0.6685 0.6588
+midnight12k ref 0.8064 0.8555 0.5970 0.6063
+midnight12k-1.0um ref 0.8013 0.8403 0.5858 0.6033
+midnight12k-cls-mean ref 0.8114 0.8776 0.6105 0.6299
+phikon ref 0.8756 0.9011 0.6690 0.6563
+resnet50-imagenet ctl 0.7009 0.7303 0.5517 0.5670
+ssl-vitb16-step10k b16 0.7575 0.7561 0.5877 0.5915
+ssl-vitb16-step35k b16 0.8290 0.8466 0.5819 0.5895
+ssl-vitb16-step40k b16 0.7934 0.8400 0.6074 0.6214
+ssl-vits16-final s16 0.7675 0.8268 0.5891 0.5954
+ssl-vits16-step20k s16 0.7382 0.7987 0.5675 0.5912
+ssl-vits16-step35k s16 0.8039 0.8313 0.5992 0.6102
+ssl-vits16-step40k s16 0.7919 0.8297 0.6199 0.5972
+ssl-vits16-step45k s16 0.7601 0.8166 0.5887 0.6123
+ssl-vits16-step50k s16 0.7808 0.8311 0.6085 0.5867
+ssl-vits16-step55k s16 0.7880 0.8450 0.5888 0.5888
+ssl-vits16-step5k s16 0.7264 0.7855 0.5697 0.5432
+ssl-vits16-step60k s16 0.7775 0.8271 0.5958 0.5924
+ssl-vits16-step65k s16 0.7588 0.8018 0.6211 0.5941
+vits16-random-init ctl 0.5900 0.6613 0.5080 0.4849
diff --git a/data/paper/book-data/m5_macros.tex b/data/paper/book-data/m5_macros.tex
new file mode 100644
index 0000000000000000000000000000000000000000..71a7d39002259128cbeea575a53cd17c92dab9c9
--- /dev/null
+++ b/data/paper/book-data/m5_macros.tex
@@ -0,0 +1,2896 @@
+% Generated by m5_data.py from data/m5_raw/ (v23 runs on the evaluation server: quoll 6993952 / SDK 0.23.0, 10 seeds).
+\providecommand\mset[2]{\expandafter\def\csname m5:#1\endcsname{#2}}
+\mset{phikon-yy}{0.872}
+\mset{phikon-yy-sd}{0.014}
+\mset{phikon-tt}{0.679}
+\mset{phikon-tt-sd}{0.012}
+\mset{phikon-ty}{0.811}
+\mset{phikon-ty-sd}{0.014}
+\mset{phikon-yt}{0.662}
+\mset{phikon-yt-sd}{0.003}
+\mset{phikon-n}{10}
+\mset{hopt-yy}{0.897}
+\mset{hopt-yy-sd}{0.012}
+\mset{hopt-tt}{0.676}
+\mset{hopt-tt-sd}{0.017}
+\mset{hopt-ty}{0.805}
+\mset{hopt-ty-sd}{0.020}
+\mset{hopt-yt}{0.653}
+\mset{hopt-yt-sd}{0.010}
+\mset{hopt-n}{10}
+\mset{midnight-yy}{0.834}
+\mset{midnight-yy-sd}{0.018}
+\mset{midnight-tt}{0.623}
+\mset{midnight-tt-sd}{0.019}
+\mset{midnight-ty}{0.727}
+\mset{midnight-ty-sd}{0.029}
+\mset{midnight-yt}{0.638}
+\mset{midnight-yt-sd}{0.005}
+\mset{midnight-n}{10}
+\mset{hibou-yy}{0.786}
+\mset{hibou-yy-sd}{0.025}
+\mset{hibou-tt}{0.626}
+\mset{hibou-tt-sd}{0.011}
+\mset{hibou-ty}{0.749}
+\mset{hibou-ty-sd}{0.027}
+\mset{hibou-yt}{0.637}
+\mset{hibou-yt-sd}{0.011}
+\mset{hibou-n}{10}
+\mset{rn50-yy}{0.700}
+\mset{rn50-yy-sd}{0.022}
+\mset{rn50-tt}{0.545}
+\mset{rn50-tt-sd}{0.017}
+\mset{rn50-ty}{0.638}
+\mset{rn50-ty-sd}{0.022}
+\mset{rn50-yt}{0.568}
+\mset{rn50-yt-sd}{0.015}
+\mset{rn50-n}{10}
+\mset{dinov2s-yy}{0.676}
+\mset{dinov2s-yy-sd}{0.029}
+\mset{dinov2s-tt}{0.495}
+\mset{dinov2s-tt-sd}{0.016}
+\mset{dinov2s-ty}{0.589}
+\mset{dinov2s-ty-sd}{0.016}
+\mset{dinov2s-yt}{0.573}
+\mset{dinov2s-yt-sd}{0.009}
+\mset{dinov2s-n}{10}
+\mset{m5b-yy}{0.836}
+\mset{m5b-yy-sd}{0.014}
+\mset{m5b-tt}{0.697}
+\mset{m5b-tt-sd}{0.014}
+\mset{m5b-ty}{0.757}
+\mset{m5b-ty-sd}{0.010}
+\mset{m5b-yt}{0.632}
+\mset{m5b-yt-sd}{0.003}
+\mset{m5b-n}{10}
+\mset{dbs40-yy}{0.814}
+\mset{dbs40-yy-sd}{0.014}
+\mset{dbs40-tt}{0.638}
+\mset{dbs40-tt-sd}{0.019}
+\mset{dbs40-ty}{0.776}
+\mset{dbs40-ty-sd}{0.011}
+\mset{dbs40-yt}{0.644}
+\mset{dbs40-yt-sd}{0.006}
+\mset{dbs40-n}{10}
+\mset{dbs45-yy}{0.784}
+\mset{dbs45-yy-sd}{0.015}
+\mset{dbs45-tt}{0.643}
+\mset{dbs45-tt-sd}{0.021}
+\mset{dbs45-ty}{0.759}
+\mset{dbs45-ty-sd}{0.017}
+\mset{dbs45-yt}{0.628}
+\mset{dbs45-yt-sd}{0.008}
+\mset{dbs45-n}{10}
+\mset{dbs50-yy}{0.816}
+\mset{dbs50-yy-sd}{0.025}
+\mset{dbs50-tt}{0.613}
+\mset{dbs50-tt-sd}{0.019}
+\mset{dbs50-ty}{0.737}
+\mset{dbs50-ty-sd}{0.030}
+\mset{dbs50-yt}{0.620}
+\mset{dbs50-yt-sd}{0.006}
+\mset{dbs50-n}{10}
+\mset{dbfinal-yy}{0.808}
+\mset{dbfinal-yy-sd}{0.016}
+\mset{dbfinal-tt}{0.648}
+\mset{dbfinal-tt-sd}{0.013}
+\mset{dbfinal-ty}{0.760}
+\mset{dbfinal-ty-sd}{0.010}
+\mset{dbfinal-yt}{0.605}
+\mset{dbfinal-yt-sd}{0.007}
+\mset{dbfinal-n}{10}
+\mset{dbrand-yy}{0.812}
+\mset{dbrand-yy-sd}{0.012}
+\mset{dbrand-tt}{0.600}
+\mset{dbrand-tt-sd}{0.018}
+\mset{dbrand-ty}{0.751}
+\mset{dbrand-ty-sd}{0.023}
+\mset{dbrand-yt}{0.600}
+\mset{dbrand-yt-sd}{0.005}
+\mset{dbrand-n}{10}
+\mset{dbm40-yy}{0.815}
+\mset{dbm40-yy-sd}{0.012}
+\mset{dbm40-tt}{0.585}
+\mset{dbm40-tt-sd}{0.018}
+\mset{dbm40-ty}{0.676}
+\mset{dbm40-ty-sd}{0.042}
+\mset{dbm40-yt}{0.592}
+\mset{dbm40-yt-sd}{0.009}
+\mset{dbm40-n}{10}
+\mset{b40-yy}{0.794}
+\mset{b40-yy-sd}{0.016}
+\mset{b40-tt}{0.601}
+\mset{b40-tt-sd}{0.018}
+\mset{b40-ty}{0.658}
+\mset{b40-ty-sd}{0.030}
+\mset{b40-yt}{0.537}
+\mset{b40-yt-sd}{0.011}
+\mset{b40-n}{10}
+\mset{b45-yy}{0.785}
+\mset{b45-yy-sd}{0.015}
+\mset{b45-tt}{0.597}
+\mset{b45-tt-sd}{0.020}
+\mset{b45-ty}{0.672}
+\mset{b45-ty-sd}{0.025}
+\mset{b45-yt}{0.551}
+\mset{b45-yt-sd}{0.010}
+\mset{b45-n}{10}
+\mset{b50-yy}{0.782}
+\mset{b50-yy-sd}{0.016}
+\mset{b50-tt}{0.608}
+\mset{b50-tt-sd}{0.014}
+\mset{b50-ty}{0.692}
+\mset{b50-ty-sd}{0.030}
+\mset{b50-yt}{0.542}
+\mset{b50-yt-sd}{0.007}
+\mset{b50-n}{10}
+\mset{bfinal-yy}{0.788}
+\mset{bfinal-yy-sd}{0.013}
+\mset{bfinal-tt}{0.619}
+\mset{bfinal-tt-sd}{0.017}
+\mset{bfinal-ty}{0.639}
+\mset{bfinal-ty-sd}{0.029}
+\mset{bfinal-yt}{0.530}
+\mset{bfinal-yt-sd}{0.007}
+\mset{bfinal-n}{10}
+\mset{m5s-yy}{0.840}
+\mset{m5s-yy-sd}{0.021}
+\mset{m5s-tt}{0.688}
+\mset{m5s-tt-sd}{0.018}
+\mset{m5s-ty}{0.745}
+\mset{m5s-ty-sd}{0.017}
+\mset{m5s-yt}{0.642}
+\mset{m5s-yt-sd}{0.005}
+\mset{m5s-n}{10}
+\mset{dsfinal-yy}{0.814}
+\mset{dsfinal-yy-sd}{0.020}
+\mset{dsfinal-tt}{0.626}
+\mset{dsfinal-tt-sd}{0.011}
+\mset{dsfinal-ty}{0.722}
+\mset{dsfinal-ty-sd}{0.023}
+\mset{dsfinal-yt}{0.601}
+\mset{dsfinal-yt-sd}{0.007}
+\mset{dsfinal-n}{10}
+\mset{ds50-yy}{0.809}
+\mset{ds50-yy-sd}{0.012}
+\mset{ds50-tt}{0.630}
+\mset{ds50-tt-sd}{0.015}
+\mset{ds50-ty}{0.740}
+\mset{ds50-ty-sd}{0.012}
+\mset{ds50-yt}{0.621}
+\mset{ds50-yt-sd}{0.008}
+\mset{ds50-n}{10}
+\mset{dsx23-yy}{0.817}
+\mset{dsx23-yy-sd}{0.019}
+\mset{dsx23-tt}{0.624}
+\mset{dsx23-tt-sd}{0.015}
+\mset{dsx23-ty}{0.722}
+\mset{dsx23-ty-sd}{0.028}
+\mset{dsx23-yt}{0.602}
+\mset{dsx23-yt-sd}{0.006}
+\mset{dsx23-n}{10}
+\mset{sfinal-yy}{0.761}
+\mset{sfinal-yy-sd}{0.027}
+\mset{sfinal-tt}{0.589}
+\mset{sfinal-tt-sd}{0.020}
+\mset{sfinal-ty}{0.589}
+\mset{sfinal-ty-sd}{0.047}
+\mset{sfinal-yt}{0.520}
+\mset{sfinal-yt-sd}{0.006}
+\mset{sfinal-n}{10}
+\mset{pd-dbs40-dsfinal-yy}{+0.000}
+\mset{pdn-dbs40-dsfinal-yy}{0.0002}
+\mset{pd-dbs40-dsfinal-yy-k}{6/10}
+\mset{pd-dbs40-dsfinal-yy-p}{0.385}
+\mset{pd-dbs40-dsfinal-tt}{+0.012}
+\mset{pdn-dbs40-dsfinal-tt}{0.0123}
+\mset{pd-dbs40-dsfinal-tt-k}{8/10}
+\mset{pd-dbs40-dsfinal-tt-p}{0.032}
+\mset{pd-dbs40-dsfinal-ty}{+0.053}
+\mset{pdn-dbs40-dsfinal-ty}{0.0535}
+\mset{pd-dbs40-dsfinal-ty-k}{10/10}
+\mset{pd-dbs40-dsfinal-ty-p}{0.001}
+\mset{pd-dbs40-dsfinal-yt}{+0.043}
+\mset{pdn-dbs40-dsfinal-yt}{0.0434}
+\mset{pd-dbs40-dsfinal-yt-k}{10/10}
+\mset{pd-dbs40-dsfinal-yt-p}{0.001}
+\mset{pd-phikon-dbs40-yy}{+0.059}
+\mset{pdn-phikon-dbs40-yy}{0.0585}
+\mset{pd-phikon-dbs40-yy-k}{10/10}
+\mset{pd-phikon-dbs40-yy-p}{0.001}
+\mset{pd-phikon-dbs40-tt}{+0.041}
+\mset{pdn-phikon-dbs40-tt}{0.0411}
+\mset{pd-phikon-dbs40-tt-k}{9/10}
+\mset{pd-phikon-dbs40-tt-p}{0.003}
+\mset{pd-phikon-dbs40-ty}{+0.035}
+\mset{pdn-phikon-dbs40-ty}{0.0354}
+\mset{pd-phikon-dbs40-ty-k}{9/10}
+\mset{pd-phikon-dbs40-ty-p}{0.002}
+\mset{pd-phikon-dbs40-yt}{+0.018}
+\mset{pdn-phikon-dbs40-yt}{0.0178}
+\mset{pd-phikon-dbs40-yt-k}{10/10}
+\mset{pd-phikon-dbs40-yt-p}{0.001}
+\mset{pd-hopt-dbs40-yy}{+0.083}
+\mset{pdn-hopt-dbs40-yy}{0.0829}
+\mset{pd-hopt-dbs40-yy-k}{10/10}
+\mset{pd-hopt-dbs40-yy-p}{0.001}
+\mset{pd-hopt-dbs40-tt}{+0.038}
+\mset{pdn-hopt-dbs40-tt}{0.0381}
+\mset{pd-hopt-dbs40-tt-k}{9/10}
+\mset{pd-hopt-dbs40-tt-p}{0.002}
+\mset{pd-hopt-dbs40-ty}{+0.030}
+\mset{pdn-hopt-dbs40-ty}{0.0296}
+\mset{pd-hopt-dbs40-ty-k}{9/10}
+\mset{pd-hopt-dbs40-ty-p}{0.002}
+\mset{pd-hopt-dbs40-yt}{+0.009}
+\mset{pdn-hopt-dbs40-yt}{0.0090}
+\mset{pd-hopt-dbs40-yt-k}{7/10}
+\mset{pd-hopt-dbs40-yt-p}{0.014}
+\mset{pd-dbs40-b40-yy}{+0.020}
+\mset{pdn-dbs40-b40-yy}{0.0196}
+\mset{pd-dbs40-b40-yy-k}{8/10}
+\mset{pd-dbs40-b40-yy-p}{0.024}
+\mset{pd-dbs40-b40-tt}{+0.037}
+\mset{pdn-dbs40-b40-tt}{0.0374}
+\mset{pd-dbs40-b40-tt-k}{10/10}
+\mset{pd-dbs40-b40-tt-p}{0.001}
+\mset{pd-dbs40-b40-ty}{+0.118}
+\mset{pdn-dbs40-b40-ty}{0.1179}
+\mset{pd-dbs40-b40-ty-k}{10/10}
+\mset{pd-dbs40-b40-ty-p}{0.001}
+\mset{pd-dbs40-b40-yt}{+0.107}
+\mset{pdn-dbs40-b40-yt}{0.1073}
+\mset{pd-dbs40-b40-yt-k}{10/10}
+\mset{pd-dbs40-b40-yt-p}{0.001}
+\mset{pd-dbs40-dbm40-yy}{$-$0.002}
+\mset{pdn-dbs40-dbm40-yy}{-0.0016}
+\mset{pd-dbs40-dbm40-yy-k}{6/10}
+\mset{pd-dbs40-dbm40-yy-p}{0.312}
+\mset{pd-dbs40-dbm40-tt}{+0.053}
+\mset{pdn-dbs40-dbm40-tt}{0.0534}
+\mset{pd-dbs40-dbm40-tt-k}{10/10}
+\mset{pd-dbs40-dbm40-tt-p}{0.001}
+\mset{pd-dbs40-dbm40-ty}{+0.100}
+\mset{pdn-dbs40-dbm40-ty}{0.1000}
+\mset{pd-dbs40-dbm40-ty-k}{10/10}
+\mset{pd-dbs40-dbm40-ty-p}{0.001}
+\mset{pd-dbs40-dbm40-yt}{+0.052}
+\mset{pdn-dbs40-dbm40-yt}{0.0522}
+\mset{pd-dbs40-dbm40-yt-k}{10/10}
+\mset{pd-dbs40-dbm40-yt-p}{0.001}
+\mset{pd-dsx23-dsfinal-yy}{+0.003}
+\mset{pdn-dsx23-dsfinal-yy}{0.0031}
+\mset{pd-dsx23-dsfinal-yy-k}{5/10}
+\mset{pd-dsx23-dsfinal-yy-p}{0.312}
+\mset{pd-dsx23-dsfinal-tt}{$-$0.001}
+\mset{pdn-dsx23-dsfinal-tt}{-0.0014}
+\mset{pd-dsx23-dsfinal-tt-k}{6/10}
+\mset{pd-dsx23-dsfinal-tt-p}{0.539}
+\mset{pd-dsx23-dsfinal-ty}{$-$0.001}
+\mset{pdn-dsx23-dsfinal-ty}{-0.0008}
+\mset{pd-dsx23-dsfinal-ty-k}{3/10}
+\mset{pd-dsx23-dsfinal-ty-p}{0.278}
+\mset{pd-dsx23-dsfinal-yt}{+0.001}
+\mset{pdn-dsx23-dsfinal-yt}{0.0012}
+\mset{pd-dsx23-dsfinal-yt-k}{4/10}
+\mset{pd-dsx23-dsfinal-yt-p}{0.539}
+\mset{pd-m5s-dsfinal-yy}{+0.027}
+\mset{pdn-m5s-dsfinal-yy}{0.0266}
+\mset{pd-m5s-dsfinal-yy-k}{9/10}
+\mset{pd-m5s-dsfinal-yy-p}{0.002}
+\mset{pd-m5s-dsfinal-tt}{+0.062}
+\mset{pdn-m5s-dsfinal-tt}{0.0618}
+\mset{pd-m5s-dsfinal-tt-k}{10/10}
+\mset{pd-m5s-dsfinal-tt-p}{0.001}
+\mset{pd-m5s-dsfinal-ty}{+0.023}
+\mset{pdn-m5s-dsfinal-ty}{0.0230}
+\mset{pd-m5s-dsfinal-ty-k}{8/10}
+\mset{pd-m5s-dsfinal-ty-p}{0.010}
+\mset{pd-m5s-dsfinal-yt}{+0.041}
+\mset{pdn-m5s-dsfinal-yt}{0.0408}
+\mset{pd-m5s-dsfinal-yt-k}{10/10}
+\mset{pd-m5s-dsfinal-yt-p}{0.001}
+\mset{pd-m5s-dbs40-yy}{+0.026}
+\mset{pdn-m5s-dbs40-yy}{0.0264}
+\mset{pd-m5s-dbs40-yy-k}{9/10}
+\mset{pd-m5s-dbs40-yy-p}{0.002}
+\mset{pd-m5s-dbs40-tt}{+0.049}
+\mset{pdn-m5s-dbs40-tt}{0.0495}
+\mset{pd-m5s-dbs40-tt-k}{10/10}
+\mset{pd-m5s-dbs40-tt-p}{0.001}
+\mset{pd-m5s-dbs40-ty}{$-$0.030}
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+\mset{inv-asof}{Mon 5 Oct, 15:00 AEDT}
+\mset{inv-eta-D1}{10:12}
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+\mset{pd-e999-jj25s3-yy-p}{0.161}
+\mset{pd-e999-jj25s3-tt}{$-$0.025}
+\mset{pd-e999-jj25s3-tt-k}{0/10}
+\mset{pd-e999-jj25s3-tt-p}{0.001}
+\mset{pd-e999-jj25s3-ty}{$-$0.010}
+\mset{pd-e999-jj25s3-ty-k}{3/10}
+\mset{pd-e999-jj25s3-ty-p}{0.138}
+\mset{pd-e999-jj25s3-yt}{$-$0.005}
+\mset{pd-e999-jj25s3-yt-k}{4/10}
+\mset{pd-e999-jj25s3-yt-p}{0.053}
+\mset{pd-e9995-jj25s3-yy}{+0.011}
+\mset{pd-e9995-jj25s3-yy-k}{7/10}
+\mset{pd-e9995-jj25s3-yy-p}{0.025}
+\mset{pd-e9995-jj25s3-tt}{$-$0.022}
+\mset{pd-e9995-jj25s3-tt-k}{2/10}
+\mset{pd-e9995-jj25s3-tt-p}{0.014}
+\mset{pd-e9995-jj25s3-ty}{$-$0.008}
+\mset{pd-e9995-jj25s3-ty-k}{2/10}
+\mset{pd-e9995-jj25s3-ty-p}{0.180}
+\mset{pd-e9995-jj25s3-yt}{$-$0.002}
+\mset{pd-e9995-jj25s3-yt-k}{4/10}
+\mset{pd-e9995-jj25s3-yt-p}{0.348}
+\mset{pd-e9995l-jj25s3-yy}{+0.006}
+\mset{pd-e9995l-jj25s3-yy-k}{6/10}
+\mset{pd-e9995l-jj25s3-yy-p}{0.216}
+\mset{pd-e9995l-jj25s3-tt}{$-$0.034}
+\mset{pd-e9995l-jj25s3-tt-k}{0/10}
+\mset{pd-e9995l-jj25s3-tt-p}{0.001}
+\mset{pd-e9995l-jj25s3-ty}{$-$0.004}
+\mset{pd-e9995l-jj25s3-ty-k}{4/10}
+\mset{pd-e9995l-jj25s3-ty-p}{0.348}
+\mset{pd-e9995l-jj25s3-yt}{$-$0.004}
+\mset{pd-e9995l-jj25s3-yt-k}{3/10}
+\mset{pd-e9995l-jj25s3-yt-p}{0.116}
+\mset{pd-e9995h-j100s-yy}{$-$0.028}
+\mset{pd-e9995h-j100s-yy-k}{1/10}
+\mset{pd-e9995h-j100s-yy-p}{0.003}
+\mset{pd-e9995h-j100s-tt}{$-$0.006}
+\mset{pd-e9995h-j100s-tt-k}{3/10}
+\mset{pd-e9995h-j100s-tt-p}{0.097}
+\mset{pd-e9995h-j100s-ty}{+0.013}
+\mset{pd-e9995h-j100s-ty-k}{7/10}
+\mset{pd-e9995h-j100s-ty-p}{0.032}
+\mset{pd-e9995h-j100s-yt}{$-$0.008}
+\mset{pd-e9995h-j100s-yt-k}{0/10}
+\mset{pd-e9995h-j100s-yt-p}{0.001}
+\mset{pd-j25s3-phikon-yy}{$-$0.024}
+\mset{pd-j25s3-phikon-yy-k}{0/10}
+\mset{pd-j25s3-phikon-yy-p}{0.001}
+\mset{pd-j25s3-phikon-tt}{+0.009}
+\mset{pd-j25s3-phikon-tt-k}{7/10}
+\mset{pd-j25s3-phikon-tt-p}{0.116}
+\mset{pd-j25s3-phikon-ty}{$-$0.013}
+\mset{pd-j25s3-phikon-ty-k}{2/10}
+\mset{pd-j25s3-phikon-ty-p}{0.042}
+\mset{pd-j25s3-phikon-yt}{$-$0.020}
+\mset{pd-j25s3-phikon-yt-k}{0/10}
+\mset{pd-j25s3-phikon-yt-p}{0.001}
+\mset{pd-jj25s3-hopt-yy}{$-$0.053}
+\mset{pd-jj25s3-hopt-yy-k}{0/10}
+\mset{pd-jj25s3-hopt-yy-p}{0.001}
+\mset{pd-jj25s3-hopt-tt}{+0.014}
+\mset{pd-jj25s3-hopt-tt-k}{8/10}
+\mset{pd-jj25s3-hopt-tt-p}{0.042}
+\mset{pd-jj25s3-hopt-ty}{+0.006}
+\mset{pd-jj25s3-hopt-ty-k}{7/10}
+\mset{pd-jj25s3-hopt-ty-p}{0.161}
+\mset{pd-jj25s3-hopt-yt}{$-$0.003}
+\mset{pd-jj25s3-hopt-yt-k}{4/10}
+\mset{pd-jj25s3-hopt-yt-p}{0.080}
+\mset{pd-m5s25-phikon-yy}{$-$0.015}
+\mset{pd-m5s25-phikon-yy-k}{2/10}
+\mset{pd-m5s25-phikon-yy-p}{0.032}
+\mset{pd-m5s25-phikon-tt}{+0.026}
+\mset{pd-m5s25-phikon-tt-k}{10/10}
+\mset{pd-m5s25-phikon-tt-p}{0.001}
+\mset{pd-m5s25-phikon-ty}{$-$0.049}
+\mset{pd-m5s25-phikon-ty-k}{0/10}
+\mset{pd-m5s25-phikon-ty-p}{0.001}
+\mset{pd-m5s25-phikon-yt}{$-$0.037}
+\mset{pd-m5s25-phikon-yt-k}{0/10}
+\mset{pd-m5s25-phikon-yt-p}{0.001}
+\mset{pd-e999s4-jj25s3-yy}{+0.021}
+\mset{pd-e999s4-jj25s3-yy-k}{9/10}
+\mset{pd-e999s4-jj25s3-yy-p}{0.007}
+\mset{pd-e999s4-jj25s3-tt}{$-$0.020}
+\mset{pd-e999s4-jj25s3-tt-k}{0/10}
+\mset{pd-e999s4-jj25s3-tt-p}{0.001}
+\mset{pd-e999s4-jj25s3-ty}{$-$0.000}
+\mset{pd-e999s4-jj25s3-ty-k}{5/10}
+\mset{pd-e999s4-jj25s3-ty-p}{0.451}
+\mset{pd-e999s4-jj25s3-yt}{$-$0.007}
+\mset{pd-e999s4-jj25s3-yt-k}{2/10}
+\mset{pd-e999s4-jj25s3-yt-p}{0.010}
+\mset{pd-e9995s4-jj25s3-yy}{+0.018}
+\mset{pd-e9995s4-jj25s3-yy-k}{7/10}
+\mset{pd-e9995s4-jj25s3-yy-p}{0.019}
+\mset{pd-e9995s4-jj25s3-tt}{$-$0.021}
+\mset{pd-e9995s4-jj25s3-tt-k}{2/10}
+\mset{pd-e9995s4-jj25s3-tt-p}{0.007}
+\mset{pd-e9995s4-jj25s3-ty}{$-$0.011}
+\mset{pd-e9995s4-jj25s3-ty-k}{2/10}
+\mset{pd-e9995s4-jj25s3-ty-p}{0.097}
+\mset{pd-e9995s4-jj25s3-yt}{$-$0.002}
+\mset{pd-e9995s4-jj25s3-yt-k}{4/10}
+\mset{pd-e9995s4-jj25s3-yt-p}{0.278}
+\mset{pd-e999l-jj25s3-yy}{+0.009}
+\mset{pd-e999l-jj25s3-yy-k}{6/10}
+\mset{pd-e999l-jj25s3-yy-p}{0.188}
+\mset{pd-e999l-jj25s3-tt}{$-$0.030}
+\mset{pd-e999l-jj25s3-tt-k}{0/10}
+\mset{pd-e999l-jj25s3-tt-p}{0.001}
+\mset{pd-e999l-jj25s3-ty}{$-$0.000}
+\mset{pd-e999l-jj25s3-ty-k}{5/10}
+\mset{pd-e999l-jj25s3-ty-p}{0.423}
+\mset{pd-e999l-jj25s3-yt}{$-$0.015}
+\mset{pd-e999l-jj25s3-yt-k}{0/10}
+\mset{pd-e999l-jj25s3-yt-p}{0.001}
+\mset{pd-e999s5-jj25s3-yy}{+0.015}
+\mset{pd-e999s5-jj25s3-yy-k}{7/10}
+\mset{pd-e999s5-jj25s3-yy-p}{0.065}
+\mset{pd-e999s5-jj25s3-tt}{$-$0.029}
+\mset{pd-e999s5-jj25s3-tt-k}{0/10}
+\mset{pd-e999s5-jj25s3-tt-p}{0.001}
+\mset{pd-e999s5-jj25s3-ty}{$-$0.013}
+\mset{pd-e999s5-jj25s3-ty-k}{2/10}
+\mset{pd-e999s5-jj25s3-ty-p}{0.007}
+\mset{pd-e999s5-jj25s3-yt}{$-$0.008}
+\mset{pd-e999s5-jj25s3-yt-k}{3/10}
+\mset{pd-e999s5-jj25s3-yt-p}{0.024}
+\mset{pd-e999s6-jj25s3-yy}{+0.013}
+\mset{pd-e999s6-jj25s3-yy-k}{7/10}
+\mset{pd-e999s6-jj25s3-yy-p}{0.032}
+\mset{pd-e999s6-jj25s3-tt}{$-$0.032}
+\mset{pd-e999s6-jj25s3-tt-k}{0/10}
+\mset{pd-e999s6-jj25s3-tt-p}{0.001}
+\mset{pd-e999s6-jj25s3-ty}{$-$0.001}
+\mset{pd-e999s6-jj25s3-ty-k}{4/10}
+\mset{pd-e999s6-jj25s3-ty-p}{0.423}
+\mset{pd-e999s6-jj25s3-yt}{$-$0.010}
+\mset{pd-e999s6-jj25s3-yt-k}{1/10}
+\mset{pd-e999s6-jj25s3-yt-p}{0.003}
+\mset{pd-mixa75-jj25s3-yy}{+0.003}
+\mset{pd-mixa75-jj25s3-yy-k}{7/10}
+\mset{pd-mixa75-jj25s3-yy-p}{0.216}
+\mset{pd-mixa75-jj25s3-tt}{$-$0.013}
+\mset{pd-mixa75-jj25s3-tt-k}{2/10}
+\mset{pd-mixa75-jj25s3-tt-p}{0.014}
+\mset{pd-mixa75-jj25s3-ty}{$-$0.018}
+\mset{pd-mixa75-jj25s3-ty-k}{2/10}
+\mset{pd-mixa75-jj25s3-ty-p}{0.024}
+\mset{pd-mixa75-jj25s3-yt}{+0.002}
+\mset{pd-mixa75-jj25s3-yt-k}{6/10}
+\mset{pd-mixa75-jj25s3-yt-p}{0.238}
+\mset{pd-mixa50-jj25s3-yy}{+0.002}
+\mset{pd-mixa50-jj25s3-yy-k}{6/10}
+\mset{pd-mixa50-jj25s3-yy-p}{0.423}
+\mset{pd-mixa50-jj25s3-tt}{$-$0.022}
+\mset{pd-mixa50-jj25s3-tt-k}{1/10}
+\mset{pd-mixa50-jj25s3-tt-p}{0.002}
+\mset{pd-mixa50-jj25s3-ty}{$-$0.017}
+\mset{pd-mixa50-jj25s3-ty-k}{2/10}
+\mset{pd-mixa50-jj25s3-ty-p}{0.042}
+\mset{pd-mixa50-jj25s3-yt}{$-$0.003}
+\mset{pd-mixa50-jj25s3-yt-k}{4/10}
+\mset{pd-mixa50-jj25s3-yt-p}{0.138}
+\mset{pd-xtf-jj25s3-yy}{$-$0.002}
+\mset{pd-xtf-jj25s3-yy-k}{4/10}
+\mset{pd-xtf-jj25s3-yy-p}{0.385}
+\mset{pd-xtf-jj25s3-tt}{$-$0.026}
+\mset{pd-xtf-jj25s3-tt-k}{2/10}
+\mset{pd-xtf-jj25s3-tt-p}{0.005}
+\mset{pd-xtf-jj25s3-ty}{$-$0.009}
+\mset{pd-xtf-jj25s3-ty-k}{2/10}
+\mset{pd-xtf-jj25s3-ty-p}{0.065}
+\mset{pd-xtf-jj25s3-yt}{$-$0.008}
+\mset{pd-xtf-jj25s3-yt-k}{1/10}
+\mset{pd-xtf-jj25s3-yt-p}{0.005}
+\mset{pd-xth-jj25s3-yy}{$-$0.000}
+\mset{pd-xth-jj25s3-yy-k}{3/10}
+\mset{pd-xth-jj25s3-yy-p}{0.385}
+\mset{pd-xth-jj25s3-tt}{$-$0.038}
+\mset{pd-xth-jj25s3-tt-k}{0/10}
+\mset{pd-xth-jj25s3-tt-p}{0.001}
+\mset{pd-xth-jj25s3-ty}{$-$0.019}
+\mset{pd-xth-jj25s3-ty-k}{2/10}
+\mset{pd-xth-jj25s3-ty-p}{0.080}
+\mset{pd-xth-jj25s3-yt}{$-$0.012}
+\mset{pd-xth-jj25s3-yt-k}{1/10}
+\mset{pd-xth-jj25s3-yt-p}{0.002}
+\mset{pd-wisea75-jj25s3-yy}{+0.011}
+\mset{pd-wisea75-jj25s3-yy-k}{7/10}
+\mset{pd-wisea75-jj25s3-yy-p}{0.116}
+\mset{pd-wisea75-jj25s3-tt}{$-$0.003}
+\mset{pd-wisea75-jj25s3-tt-k}{3/10}
+\mset{pd-wisea75-jj25s3-tt-p}{0.246}
+\mset{pd-wisea75-jj25s3-ty}{$-$0.011}
+\mset{pd-wisea75-jj25s3-ty-k}{2/10}
+\mset{pd-wisea75-jj25s3-ty-p}{0.065}
+\mset{pd-wisea75-jj25s3-yt}{$-$0.003}
+\mset{pd-wisea75-jj25s3-yt-k}{3/10}
+\mset{pd-wisea75-jj25s3-yt-p}{0.097}
+\mset{pd-wisea50-jj25s3-yy}{+0.015}
+\mset{pd-wisea50-jj25s3-yy-k}{6/10}
+\mset{pd-wisea50-jj25s3-yy-p}{0.084}
+\mset{pd-wisea50-jj25s3-tt}{+0.003}
+\mset{pd-wisea50-jj25s3-tt-k}{6/10}
+\mset{pd-wisea50-jj25s3-tt-p}{0.348}
+\mset{pd-wisea50-jj25s3-ty}{$-$0.015}
+\mset{pd-wisea50-jj25s3-ty-k}{2/10}
+\mset{pd-wisea50-jj25s3-ty-p}{0.019}
+\mset{pd-wisea50-jj25s3-yt}{$-$0.004}
+\mset{pd-wisea50-jj25s3-yt-k}{3/10}
+\mset{pd-wisea50-jj25s3-yt-p}{0.065}
+\mset{pd-wisea75-m5s25-yy}{$-$0.003}
+\mset{pd-wisea75-m5s25-yy-k}{4/10}
+\mset{pd-wisea75-m5s25-yy-p}{0.278}
+\mset{pd-wisea75-m5s25-tt}{$-$0.017}
+\mset{pd-wisea75-m5s25-tt-k}{1/10}
+\mset{pd-wisea75-m5s25-tt-p}{0.005}
+\mset{pd-wisea75-m5s25-ty}{+0.038}
+\mset{pd-wisea75-m5s25-ty-k}{10/10}
+\mset{pd-wisea75-m5s25-ty-p}{0.001}
+\mset{pd-wisea75-m5s25-yt}{+0.022}
+\mset{pd-wisea75-m5s25-yt-k}{10/10}
+\mset{pd-wisea75-m5s25-yt-p}{0.001}
+\mset{pd-wisea50-m5s25-yy}{+0.002}
+\mset{pd-wisea50-m5s25-yy-k}{6/10}
+\mset{pd-wisea50-m5s25-yy-p}{0.539}
+\mset{pd-wisea50-m5s25-tt}{$-$0.012}
+\mset{pd-wisea50-m5s25-tt-k}{2/10}
+\mset{pd-wisea50-m5s25-tt-p}{0.042}
+\mset{pd-wisea50-m5s25-ty}{+0.035}
+\mset{pd-wisea50-m5s25-ty-k}{10/10}
+\mset{pd-wisea50-m5s25-ty-p}{0.001}
+\mset{pd-wisea50-m5s25-yt}{+0.020}
+\mset{pd-wisea50-m5s25-yt-k}{10/10}
+\mset{pd-wisea50-m5s25-yt-p}{0.001}
+\mset{best-yy}{0.871}
+\mset{best-yy-gap}{$-$0.002}
+\mset{best-yy-who}{JEPA round 2 on the 50k start}
+\mset{best-tt}{0.705}
+\mset{best-tt-gap}{+0.026}
+\mset{best-tt-who}{M5.G ViT-S, 25k snapshot (main)}
+\mset{best-ty}{0.812}
+\mset{best-ty-gap}{+0.001}
+\mset{best-ty-who}{JEPA round 2, the chain from round 1 seed 4}
+\mset{best-yt}{0.656}
+\mset{best-yt-gap}{$-$0.006}
+\mset{best-yt-who}{JEPA from M6.D ViT-S, seed 4, step 2,000 (mid-schedule)}
diff --git a/data/paper/book-data/trackA_dot_budget-yale.dat b/data/paper/book-data/trackA_dot_budget-yale.dat
new file mode 100644
index 0000000000000000000000000000000000000000..251959fcdf080be7cc0053b624d566a311497168
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_budget-yale.dat
@@ -0,0 +1,3 @@
+y group mean sd lo hi
+1 s16 0.8268 0.0484 0.6847 0.9397
+2 s16 0.8105 0.0260 0.6575 0.9331
diff --git a/data/paper/book-data/trackA_dot_budget-yale_rows.dat b/data/paper/book-data/trackA_dot_budget-yale_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..2fbb6bfc76506b5c5b805b4ca99b2f21e291cbf8
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_budget-yale_rows.dat
@@ -0,0 +1,3 @@
+y	label
+1	ssl-vits16 final, 400 tiles
+2	p9final-all
diff --git a/data/paper/book-data/trackA_dot_eq-ish.dat b/data/paper/book-data/trackA_dot_eq-ish.dat
new file mode 100644
index 0000000000000000000000000000000000000000..fbd16845d94bec8c30b9eed8734125711764ce8f
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_eq-ish.dat
@@ -0,0 +1,5 @@
+y group mean sd lo hi
+1 ref 0.7646 0.1067 0.5238 0.9639
+2 s16 0.6984 0.0721 0.4253 0.9396
+3 dst 0.6899 0.1029 0.4176 0.9363
+4 ref 0.6746 0.1186 0.4111 0.9183
diff --git a/data/paper/book-data/trackA_dot_eq-ish_rows.dat b/data/paper/book-data/trackA_dot_eq-ish_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..8c47509e3047bbd4776884a3bbfabffddf302a39
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_eq-ish_rows.dat
@@ -0,0 +1,5 @@
+y	label
+1	H-optimus-0, card norm., CLS
+2	ssl-vits16 final (93k)
+3	D13: Midnight-12k teacher, 20k steps
+4	Midnight-12k, CLS
diff --git a/data/paper/book-data/trackA_dot_her2-tcga.dat b/data/paper/book-data/trackA_dot_her2-tcga.dat
new file mode 100644
index 0000000000000000000000000000000000000000..8d30533363d865f57dc8645473a782c52ff0994e
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_her2-tcga.dat
@@ -0,0 +1,36 @@
+y group mean sd lo hi
+1 ref 0.6694 0.0718 0.5518 0.7794
+2 ref 0.6655 0.0603 0.5463 0.7786
+3 dstb 0.6627 0.0615 0.5450 0.7749
+4 ref 0.6597 0.0722 0.5444 0.7702
+5 ref 0.6588 0.0578 0.5411 0.7751
+6 best 0.6578 0.0732 0.5416 0.7665
+7 ref 0.6563 0.0578 0.5370 0.7675
+8 dst 0.6532 0.0821 0.5349 0.7647
+9 dst 0.6512 0.0497 0.5315 0.7651
+10 dstb 0.6452 0.0643 0.5280 0.7548
+11 ref 0.6299 0.0484 0.5101 0.7440
+12 b16 0.6214 0.0686 0.4948 0.7370
+13 dst 0.6159 0.0404 0.4960 0.7301
+14 s16 0.6123 0.0664 0.4885 0.7281
+15 s16 0.6102 0.0557 0.4854 0.7308
+16 ref 0.6063 0.0340 0.4858 0.7240
+17 ref 0.6050 0.0478 0.4802 0.7244
+18 ref 0.6033 0.0543 0.4831 0.7219
+19 s16 0.5972 0.0696 0.4754 0.7143
+20 s16 0.5954 0.0515 0.4697 0.7116
+21 s16 0.5941 0.0610 0.4687 0.7109
+22 s16 0.5924 0.0495 0.4678 0.7107
+23 b16 0.5915 0.0503 0.4697 0.7101
+24 s16 0.5912 0.0467 0.4671 0.7125
+25 dst 0.5905 0.0794 0.4695 0.7067
+26 b16 0.5895 0.0613 0.4668 0.7071
+27 s16 0.5888 0.0358 0.4633 0.7083
+28 s16 0.5867 0.0490 0.4603 0.7057
+29 dst 0.5719 0.0435 0.4470 0.6916
+30 ctl 0.5670 0.0681 0.4442 0.6900
+31 dst 0.5601 0.0971 0.4381 0.6808
+32 dst 0.5578 0.0510 0.4343 0.6741
+33 s16 0.5432 0.0547 0.4156 0.6662
+34 ctl 0.5410 0.0813 0.4177 0.6604
+35 ctl 0.4849 0.0536 0.3597 0.6067
diff --git a/data/paper/book-data/trackA_dot_her2-tcga_rows.dat b/data/paper/book-data/trackA_dot_her2-tcga_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..8411837052fa949805c28eb0214e886f2d6d2206
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_her2-tcga_rows.dat
@@ -0,0 +1,36 @@
+y	label
+1	H-optimus-0, card norm., CLS+mean
+2	H-optimus-0, card norm., CLS, 1.0\,\textmu m/px
+3	\textbf{\textcolor{hrB16!80!black}{D18: H-optimus-0, ViT-B/16, 5k, from step 40k}}
+4	H-optimus-0, card norm., CLS
+5	H-optimus-0, ImageNet norm. (Aug.)
+6	\textbf{\textcolor{hrBest}{D16: H-optimus-0, 5k, from final}}
+7	Phikon v1, ViT-B/16 (Aug.)
+8	D14: H-optimus-0 teacher, 20k steps
+9	D16: H-optimus-0, 5k, from step 50k
+10	D18: H-optimus-0, ViT-B/16, 5k, from step 45k
+11	Midnight-12k, CLS+mean
+12	ssl-vitb16 step 40k
+13	D13: Midnight-12k teacher, 20k steps
+14	ssl-vits16 step 45k
+15	ssl-vits16 step 35k
+16	Midnight-12k, CLS
+17	Hibou-B (Aug.)
+18	Midnight-12k, CLS, 1.0\,\textmu m/px
+19	ssl-vits16 step 40k
+20	ssl-vits16 final (93k)
+21	ssl-vits16 step 65k
+22	ssl-vits16 step 60k
+23	ssl-vitb16 step 10k
+24	ssl-vits16 step 20k
+25	D16: H-optimus-0, 5k, from random
+26	ssl-vitb16 step 35k
+27	ssl-vits16 step 55k
+28	ssl-vits16 step 50k
+29	D15: Midnight, 5k, from step 50k
+30	ResNet-50, ImageNet (Aug.)
+31	D15: Midnight, 5k, from final
+32	D15: Midnight, 5k, from random
+33	ssl-vits16 step 5k
+34	DINOv2 ViT-S/14 (natural images)
+35	random weights, ViT-S/16
diff --git a/data/paper/book-data/trackA_dot_her2-yale.dat b/data/paper/book-data/trackA_dot_her2-yale.dat
new file mode 100644
index 0000000000000000000000000000000000000000..66c92832792524681ff58dd9eafe01608af0301c
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_her2-yale.dat
@@ -0,0 +1,39 @@
+y group mean sd lo hi
+1 ref 0.9134 0.0673 0.8171 0.9801
+2 ref 0.9129 0.0614 0.8159 0.9797
+3 ref 0.9105 0.0575 0.8083 0.9792
+4 ref 0.9068 0.0581 0.8039 0.9780
+5 ref 0.9011 0.0588 0.7955 0.9749
+6 ref 0.8776 0.0636 0.7567 0.9670
+7 best 0.8697 0.0526 0.7404 0.9661
+8 dstb 0.8563 0.0523 0.7233 0.9562
+9 dst 0.8561 0.0459 0.7196 0.9575
+10 ref 0.8555 0.0430 0.7221 0.9592
+11 ref 0.8534 0.0619 0.7168 0.9560
+12 dstb 0.8513 0.0463 0.7131 0.9557
+13 b16 0.8466 0.0473 0.7104 0.9523
+14 dst 0.8455 0.0522 0.7120 0.9484
+15 s16 0.8450 0.0571 0.7113 0.9499
+16 ref 0.8403 0.0630 0.6999 0.9510
+17 b16 0.8400 0.0600 0.7060 0.9464
+18 dstb 0.8395 0.0488 0.7010 0.9466
+19 dst 0.8384 0.0661 0.6950 0.9467
+20 s16 0.8313 0.0576 0.6912 0.9424
+21 s16 0.8311 0.0708 0.6934 0.9406
+22 s16 0.8297 0.0698 0.6858 0.9414
+23 dst 0.8282 0.0768 0.6896 0.9381
+24 s16 0.8271 0.0469 0.6819 0.9411
+25 s16 0.8268 0.0484 0.6847 0.9397
+26 b16 0.8200 0.0468 0.6712 0.9402
+27 s16 0.8166 0.0577 0.6669 0.9323
+28 b16 0.8134 0.0514 0.6647 0.9337
+29 dst 0.8092 0.0595 0.6584 0.9315
+30 dst 0.8079 0.0443 0.6530 0.9309
+31 dst 0.8068 0.0569 0.6568 0.9323
+32 s16 0.8018 0.0452 0.6458 0.9284
+33 s16 0.7987 0.0733 0.6483 0.9223
+34 ctl 0.7939 0.0569 0.6338 0.9258
+35 s16 0.7855 0.0834 0.6283 0.9173
+36 b16 0.7561 0.0539 0.5906 0.8958
+37 ctl 0.7303 0.0726 0.5603 0.8800
+38 ctl 0.6613 0.0651 0.4784 0.8224
diff --git a/data/paper/book-data/trackA_dot_her2-yale_rows.dat b/data/paper/book-data/trackA_dot_her2-yale_rows.dat
new file mode 100644
index 0000000000000000000000000000000000000000..7fe16a921b927d4ad2f73cf45ffe67a8ead6b581
--- /dev/null
+++ b/data/paper/book-data/trackA_dot_her2-yale_rows.dat
@@ -0,0 +1,39 @@
+y	label
+1	H-optimus-0, card norm., CLS, 1.0\,\textmu m/px
+2	H-optimus-0, card norm., CLS
+3	H-optimus-0, card norm., CLS+mean
+4	H-optimus-0, ImageNet norm. (Aug.)
+5	Phikon v1, ViT-B/16 (Aug.)
+6	Midnight-12k, CLS+mean
+7	\textbf{\textcolor{hrBest}{D16: H-optimus-0, 5k, from final}}
+8	\textbf{\textcolor{hrB16!80!black}{D18: H-optimus-0, ViT-B/16, 5k, from step 40k}}
+9	D14: H-optimus-0 teacher, 20k steps
+10	Midnight-12k, CLS
+11	Hibou-B (Aug.)
+12	D18: H-optimus-0, ViT-B/16, 5k, from step 50k
+13	ssl-vitb16 step 35k
+14	D16: H-optimus-0, 5k, from step 50k
+15	ssl-vits16 step 55k
+16	Midnight-12k, CLS, 1.0\,\textmu m/px
+17	ssl-vitb16 step 40k
+18	D18: H-optimus-0, ViT-B/16, 5k, from step 45k
+19	D13: Midnight-12k teacher, 20k steps
+20	ssl-vits16 step 35k
+21	ssl-vits16 step 50k
+22	ssl-vits16 step 40k
+23	D16: H-optimus-0, 5k, from random
+24	ssl-vits16 step 60k
+25	ssl-vits16 final (93k)
+26	ssl-vitb16 step 45k
+27	ssl-vits16 step 45k
+28	ssl-vitb16 step 20k
+29	D15: Midnight, 5k, from final
+30	D15: Midnight, 5k, from step 50k
+31	D15: Midnight, 5k, from random
+32	ssl-vits16 step 65k
+33	ssl-vits16 step 20k
+34	DINOv2 ViT-S/14 (natural images)
+35	ssl-vits16 step 5k
+36	ssl-vitb16 step 10k
+37	ResNet-50, ImageNet (Aug.)
+38	random weights, ViT-S/16
diff --git a/data/paper/paper-data/fig_ladder.tex b/data/paper/paper-data/fig_ladder.tex
new file mode 100644
index 0000000000000000000000000000000000000000..0b5c72154d1f0b915563f7da8e52daf235179622
--- /dev/null
+++ b/data/paper/paper-data/fig_ladder.tex
@@ -0,0 +1,17 @@
+% Generated by docs/paper/paper_data.py
+\nextgroupplot[title={\YY}]
+\addplot[blue!70!black, mark=*, mark size=1.8pt] coordinates {(0,0.7610) (1,0.8580) (2,0.8497) (3,0.8440)};
+\addplot[red!70!black, dashed, domain=-0.3:3.3, samples=2] {0.8720};
+\addplot[black!50, dotted, domain=-0.3:3.3, samples=2] {0.8970};
+\nextgroupplot[title={\TT}]
+\addplot[blue!70!black, mark=*, mark size=1.8pt] coordinates {(0,0.5890) (1,0.7050) (2,0.6877) (3,0.6900)};
+\addplot[red!70!black, dashed, domain=-0.3:3.3, samples=2] {0.6790};
+\addplot[black!50, dotted, domain=-0.3:3.3, samples=2] {0.6760};
+\nextgroupplot[title={\TY}]
+\addplot[blue!70!black, mark=*, mark size=1.8pt] coordinates {(0,0.5890) (1,0.7620) (2,0.7950) (3,0.8120)};
+\addplot[red!70!black, dashed, domain=-0.3:3.3, samples=2] {0.8110};
+\addplot[black!50, dotted, domain=-0.3:3.3, samples=2] {0.8050};
+\nextgroupplot[title={\YT}]
+\addplot[blue!70!black, mark=*, mark size=1.8pt] coordinates {(0,0.5200) (1,0.6250) (2,0.6423) (3,0.6500)};
+\addplot[red!70!black, dashed, domain=-0.3:3.3, samples=2] {0.6620};
+\addplot[black!50, dotted, domain=-0.3:3.3, samples=2] {0.6530};
diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s1.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..659f6aace18aece6b254b44188f6823e60bcbdd3
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s1.csv
@@ -0,0 +1,7 @@
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s10.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s10.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3dfa597b330ceecf31b297a5b1ea95a42966dae1
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s10.csv
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s2.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..15c34358bf0b2c3028c44d4565bfef8aa440855c
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s2.csv
@@ -0,0 +1,7 @@
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s3.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..23685e73ef4670b02ed1391a753789d13cd3a5fd
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s3.csv
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s4.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0adf4f698737b9d164e549277180ba93834b327d
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s4.csv
@@ -0,0 +1,7 @@
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s5.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6929cf31f846859c910abc0d9bc8bff122bbd6f6
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s5.csv
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diff --git a/data/paper/paper-data/her2tasks/fish/dinov2s/s6.csv b/data/paper/paper-data/her2tasks/fish/dinov2s/s6.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8ac448837f287c23e441454bf5cf49a326bcc52e
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/fish/dinov2s/s6.csv
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diff --git a/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s10/predictions.csv b/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s10/predictions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1c448cc100d5930a76dfb56226b039ed1162d19c
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s10/predictions.csv
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diff --git a/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s2/predictions.csv b/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s2/predictions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3d6e48710aa7af890d889c73f19050a00e98d493
--- /dev/null
+++ b/data/paper/paper-data/her2tasks/reader/v23-t2y-dinov2s-s2/predictions.csv
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