Habitat suitability modeling based on a spatiotemporal model: an example for cusk in the Gulf of Maine
Bibliographic record
Abstract
Habitat use and distribution is a critical aspect in the management and conservation of a species, particularly for those in decline. Habitat suitability indices (HSI) are a common method of habitat mapping dependent on empirical data that can easily lead to misunderstanding the spatiotemporal dynamics of marine species experiencing population decline and density-dependent catchability within surveys. This is especially true when only a single monitoring program with limited spatiotemporal coverage is used. A delta-generalized linear mixed model was used to combine trawl and longline surveys to predict density estimates for cusk (Brosme brosme) in unsampled locations for use in HSIs. Catchability was estimated for longline and trawl gear without having an estimate of area fished for the longline survey. HSIs performed better using model-based density estimates from multiple surveys compared with sample-based abundance indices from a single survey. The increased spatial resolution can better inform the HSIs by providing information where the survey programs did not sample. This study provides a novel approach for integrating data from different monitoring programs for habitat modeling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".