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: Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation contexts in guppiesThe work is in press in Nature Communications (2023). Link pending publicationAuthors: Alberto Corral-Lopez*1,2,3,4, Alexander Kotrschal2,5, Alexander Szorkovszky6, Maddi Garate-Olaizola2,4, James Herbert-Read7,8, Wouter van der Bijl1, Maksym Romenskyy9, Hong-Li Zeng10, Severine Denise Buechel2,5, Ada Fontrodona-Eslava2,11, Kristiaan Pelckmans12, Judith E. Mank1, Niclas Kolm2*corresponding author: alberto.corral@ebc.uu.seAffiliations1 - 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.856 | 0.562 |
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 source (direct Gemma or distilled Codex), 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".