Inverse modelling of trophic flows through an entire ecosystem: the northern Gulf of St. Lawrence in the mid-1980s
Bibliographic record
Abstract
Mass-balance models using inverse methodology have been constructed for the northern Gulf of St. Lawrence ecosystem in the mid-1980s, before the groundfish collapse. The results highlight the effects of the major mortality sources (fishing, predation, and other sources of mortality) on the fish and invertebrate communities. Main predators of fish were large cod (Gadus morhua) followed by redfish (Sebastes spp.), capelin (Mallotus villosus), and fisheries. Large cod were the most important predator of small cod, with cannibalism accounting for at least 44% of the mortality of small cod. The main predators of large cod were harp (Phoca groenlandica) and grey (Halichoerus grypus) seals. However, predation represented only 2% of total mortality on large cod. Mortality other than predation dominated the mortality processes at 52% of the total, while the fishery represented 46%. Tests were performed to identify possible sources of this unexplained mortality. The only way to significantly reduce unexplained mortality on large cod in the model was to increase landings of large cod above those reported. This suggests that fishing mortality was substantially underestimated in the mid-1980s, just before the demise of a cod stock that historically was the second largest in the northwest Atlantic.
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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 | 0.001 | 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".