Evaluating a habitat template model's predictions of marine fish diversity on the Scotian Shelf and Bay of Fundy, Northwest Atlantic
Bibliographic record
Abstract
Abstract Fisher, J. A. D., Frank, K. T., Kostylev, V. E., Shackell, N. L., Horsman, T., and Hannah, C. G. 2011. Evaluating a habitat template model's predictions of marine fish diversity on the Scotian Shelf and Bay of Fundy, Northwest Atlantic. – ICES Journal of Marine Science, 68: 2096–2105. Habitat template models that integrate physical, chemical, and biological data have the potential to explain geographic variation in life-history traits within assemblages and to predict locations where species will be most vulnerable to anthropogenic impact. A previously developed model based on spatial variations in scope for growth (SG) and natural disturbance (ND) for the Scotian Shelf/Bay of Fundy was tested for its ability to predict fish life-history traits, species diversity, and community composition using trawl survey data. Of 30 dominant fish species, large, slow-growing ones most vulnerable to overexploitation tended to be found in naturally stable habitats. Among consistently co-occurring groups, functional differentiation, rather than similarity of life history, characterized assemblages, whereas among survey strata, species richness peaked at intermediate levels of ND and species evenness increased with average SG. Community composition was significantly correlated with both habitat variables, declined by 40% through time preceding structural changes on the eastern Scotian Shelf, but geographic distance, depth, and bottom temperature remained more strongly related to composition. The results revealed compelling matches between template predictions and patterns of fish species diversity, but low variation in diversity of life history, and mobility of fish may account for some of the weaker matches to model predictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".