Local ecological knowledge and marine fisheries research: the case of white hake (<i>Urophycis tenuis</i>) predation on juvenile American lobster (<i>Homarus americanus</i>)
Bibliographic record
Abstract
Southern Gulf of St. Lawrence fish harvesters voiced the concern that white hake (Urophycis tenuis) were jeopardizing the recruitment of juvenile American lobster (Homarus americanus), through predation, into the commercially exploitable population. The harvesters insisted that marine science was not documenting this situation, since sampling was being conducted in the wrong places and at the wrong times of year. This paper reports on the results arising from a 2-year collaborative and interdisciplinary research project designed to examine fish harvesters' concerns. Several social research methodologies were used to identify and interview local knowledge experts about where and when sampling should occur. Following harvesters' advice, white hake stomachs were sampled over a 2-year period. Contrary to harvester expectations, American lobster was not found in any of the 3080 white hake stomachs sampled. Yet, harvesters' advice did result in successful sampling from within the places recommended and at the times of year specified. The research also demonstrates an interdisciplinary and collaborative approach that generates meaningful research results while incorporating marine harvester local knowledge and addressing their concerns.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".