anyFish: an open-source software to generate animated fish models for behavioural studies
Bibliographic record
Abstract
Problem: Using an experimental approach to study behaviours based on visual signals is severely limited due to the difficulty of combining realistic models (e.g. live fish) with the manipulation of signals in isolation. Solution: Computer animations allow the manipulation of a single cue while maintaining the rest of the behavioural phenotype of a realistic three-dimensional (3D) model. Software: We introduce the open-source software anyFish for the creation of 3D-animated fish. Both the animated model and its behaviour can be modified by the end-user to suit specific needs. Applications: Computer-animated fish facilitate the identification of factors influencing behaviours based on visual cues, and ultimately the way they both drive and respond to selection. For our research, we vary nuptial colour and size and shape of animated male stickleback to quantify female choice for these characters. The software has many other applications, as other fish species can be animated and characters like swimming speed and direction can be manipulated as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".