You are what you eat, whenever or wherever you eat it: an integrative analysis of fish food habits in Canadian and U.S.A. waters
Bibliographic record
Abstract
The degree to which fish diet differs by season and area, particularly over broad scales, was examined for the first time in temperate, contiguous north-west Atlantic Ocean waters by comparing food habit data for 10 species of fishes collected concurrently during the spring and autumn surveys in the U.S.A. (Gulf of Maine proper and Georges Bank) and in the summer survey in Canada (western Scotian Shelf and Bay of Fundy). For most species, there was a general concurrence among the three seasons and four areas: summer diets had the same dominant prey items as spring and autumn diets. Although a suite of multivariate analyses did elucidate some differences in specific proportions of the diet for these species across seasons and areas, the main prey did not substantially change for most of these species. These results suggest that there are (1) minimal differences in diet across season for these species at these taxonomic resolutions, (2) there are minimal differences in diet geographically for these species and (3) differences across species, as expected, are important. Many fisheries ecosystem and multispecies models are dependent on food habit data, where resolving seasonal and spatial differences in diet remains an important consideration; however, the present work implies that amalgamated estimates of diet from seasonal surveys may be a reasonable approach when no finer seasonal resolution exists, as long as due diligence is exercised.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".