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 published in Nature Communications (2023)Authors: 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 Romenskyy2,9, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.005 |
| Insufficient payload (model declined to judge) | 0.831 | 0.524 |
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".