Supporting Data and Code for Corral-Lopez et al. manuscript: <b>Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation</b><b> </b><b>contexts in guppies</b>
Bibliographic record
Abstract
Supporting Data and Code for Corral-Lopez et al. manuscript: <b>Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation</b><b> </b><b>contexts in guppies</b>The work is in press in Nature Communications (2023). Link pending publicationAuthors:<br>Alberto Corral-Lopez*<sup>1,2,3,4</sup>, Alexander Kotrschal<sup>2,5</sup>, Alexander Szorkovszky<sup>6</sup>, Maddi Garate-Olaizola<sup>2,4</sup>, James Herbert-Read<sup>7,8</sup>, Wouter van der Bijl<sup>1</sup>, Maksym Romenskyy<sup>9</sup>, Hong-Li Zeng<sup>10</sup>, Severine Denise Buechel<sup>2,5</sup>, Ada Fontrodona-Eslava<sup>2,11</sup>, Kristiaan Pelckmans<sup>12</sup>, Judith E. Mank<sup>1</sup>, Niclas Kolm<sup>2</sup>*corresponding author: alberto.corral@ebc.uu.se<b>Affiliations</b>1 - Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada2 - Department of Zoology/Ethology, Stockholm University, Stockholm, Sweden.3 - Division of Biosciences, University College London, London, United Kingdom4 – Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden5- Behavioural Ecology, Wageningen University & Research, Wageningen, Netherlands.6 - RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway7 - Department of Zoology, University of Cambridge, Cambridge, UK.8 - Aquatic Ecology, Lund University, Lund, Sweden.9 - Department of Life Sciences, Imperial College London, London, UK.10 - School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China.11 - Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK12 - Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".