Short-Range Movement Pattern of Amphidromous Lagoon Fish Schools: Ecological Applications
Bibliographic record
Abstract
High-resolution multibeam sonar allows estimating movements of pelagic fish schools at short range. Taking advantage of this methodology, we calculated a Straightness Index (SI) to quantify the proportion of schools migrating actively from those residents in lagoon channels. This information enhances our knowledge of both fish school displacements and migration processes, which are essential to improve our understanding of ecosystem functioning. Most fish schools (65%) exhibited a SI value demonstrating oriented swimming behavior through the channels displayed by schools reaching the sea during fall migration. This trend appears as an intrinsic property of school movements, allowing monitoring of the school migration process in a channel to provide information for manager vs. fishing regulation measures or lagoon planning. The result strengthens the ‘multi-transit’ hypothesis, as 35% of schools show sinuous trajectories representative of schools staying in the channel or displaying high exploratory behaviors. Lastly, the fish school Exploration Swimming Speed (ESS) was tested as a fishery-independent sampling method to evaluate the proportion of different fish species monitored using hydroacoustics. This approach demonstrates the interest in using swimming behavioral characteristics of fish schools for ecological and management purposes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".