What has Canada caught, and how much is left? Reconstructing and assessing fisheries in three oceans
Bibliographic record
Abstract
Canada’s marine fisheries occur in three oceans, designated by Pacific, Arctic and Atlantic Exclusive Economic Zones (EEZs), where management bodies utilize catch records in order to make decisions regarding the future of their fisheries. However, current catch reporting systems and stock assessment processes are flawed, as catch records are missing key fishery components and assessments may use time series that do not represent the full scale of change. These shortcomings can directly impact the perception of healthy fisheries and influence future management decisions. This research provides a comprehensive catch record for all available marine populations in Canada’s three surrounding EEZs from 1950-2017 in order to estimate their current status and provide reference points that may be useful for managers to secure marine resources for the future. Catch reconstructions initially done by the Sea Around Us group and external collaborators, are refined and updated to 2017. Using reconstructed time series, the most recent ‘CMSY’ stock assessment method allows reference points to be estimated and reveals that the majority of Canadian fisheries need rebuilding. As well, ‘CMSY’ analyses are used to investigate shifting baseline effects on selected official stock assessments that exhibit shortened catch time series. Overall, this research contributes to improving scientific baselines in order to gain a better understanding of Canadian fisheries from a historical and managerial perspective.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".