Using temporally explicit habitat suitability models to infer the migratory pattern of a large mobile shark
Bibliographic record
Abstract
Conservation and management measures for large mobile sharks are more effective when information on migratory patterns and environmental cues are known. In the absence of long-term monitoring data or tracking programs, available information is based on occasional catch or sighting records from a variety of sources, usually constrained in space and time. This study demonstrates the utility of developing temporally explicit habitat suitability (HS) models to infer the migratory pattern of large mobile sharks. Bimonthly presence-only HS models (MaxEnt algorithm) were developed for the broadnose sevengill shark (Notorynchus cepedianus) in the southwest Atlantic based on an exhaustive collection of data records and ecologically relevant predictors. The six final models showed good predictive power and were evaluated with independent data. A migratory pattern characterized by two main displacements was inferred from the models. We show that HS models can be applied as a no-cost, desk-based alternative to infer broadscale movements of large mobile sharks. This methodology is relevant as an important first step toward informing management plans in data-poor and financially limited regions or regions under urgent conservation need.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".