Modelling the spatial-temporal distributions and associated determining factors of a keystone pelagic fish
Bibliographic record
Abstract
Abstract Mobile pelagic species habitat is structured around dynamic oceanographic and ecological processes which operate and interact horizontally and vertically throughout the water column and change over time. However, pelagic species movements and distributions are often poorly understood. We use the Maxent species distribution model to assess how changes in the relative importance of modelled oceanographic (e.g., temperature) and climatic variables (e.g., the North Atlantic Oscillation) over 17-years affect the monthly average horizontal and vertical distribution of a keystone pelagic forage species, Atlantic Canadian capelin ( Mallotus villosus ). We show the range and distribution of capelin occurrence probabilities vary across horizontal and vertical axes over time, with binary presence/absence predictions indicating capelin occupy between 0.72% (April) and 3.45% (November) of the total modelled space. Furthermore, our analysis reveals that the importance of modelled oceanographic variables, such as temperature, vary between months (44% permutation importance in August to 2% in May). By capturing the spatial dynamics of capelin over horizontal, vertical, and temporal axes, our analysis builds on work that improves our understanding and predictive modelling ability of pelagic species distributions under current and future conditions for pro-active ecosystem-based management.
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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 | 0.000 | 0.000 |
| 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".