Evaluating the knowledge base for expanding low-trophic-level fisheries in Atlantic Canada
Bibliographic record
Abstract
Over the last two decades, low-trophic-level fisheries have rapidly expanded in Atlantic Canada, largely compensating for collapsed groundfisheries; however, concerns have been raised regarding the limited background knowledge for many newly targeted species and their overexploitation in other regions. Using government stock assessments, we evaluated the amount of information available to assess population, fisheries, and ecosystem status in emerging (new since 1988), developing (expanding since 1988), and established fisheries on the Scotian Shelf. Emerging fisheries had significantly lower levels of population knowledge than developing and established fisheries. Importantly, knowledge was often lacking in basic population parameters such as growth rates, current biomass, and geographic range. In contrast, ecosystem knowledge, such as habitat disruption and recovery, was higher in emerging than established fisheries. Overall, quantitative knowledge was positively related to fishery value and greatest for 30- to 100-year-old fisheries. Although the number of government and general scientific publications greatly increased since 1990 for developing and established fisheries, publications for emerging fisheries remained at low levels. Emerging fisheries represent important socio-economic value in Atlantic Canada but may be progressing too rapidly for adequate knowledge to be gained, presenting a risk for their sustainable development.
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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 | 0.022 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".