Super-individual based model for fish migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the most remarkable characteristics of collective motion of fish is the emergence of complex migration patterns in which swimming fish are synchronised by remaining together and moving in the same direction. These migration patterns, referred to as fish schools, are often explained using individual based models (IBM’s) that focus on interactions between single individuals. The IBM’s appear to be realistic and robust; however, they are computationally unable to efficiently describe migration of large groups of fish. Here, an approach for developing computationally efficient super-individual based models from simple individual based models for fish migration is proposed. This approach accentuates on ecological mechanisms underlying collective motion of fish, and interaction between them; it explicitly incorporates such important mechanisms in collective motion of fish as fish school splitting and merging.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 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 it