The history of ocean resources: modeling cod biomass using historical records
Bibliographic record
Abstract
Managing the remnants of the ocean's resources is a critical issue worldwide, but evidence for what constitutes a healthy fish population remains controversial. Here, we use historical sources to understand ecosystem trends and establish a biomass estimate for a key marine species prior to the industrialization of fishing. Declining trajectories have been described for predatory fishes and complex coral reef systems globally, but few numerical estimates of past abundance exist. We combined historical research methods and population modeling to estimate the biomass of cod on Canada's Scotian Shelf in 1852. Mid 19th-century New England fishing logs offer geographically specific daily catch records, describing fleet activity on fishing grounds with negligible incentive to falsify records. Combined with ancillary fishery documents, these logs provide a solid, reliable basis for stock assessment. Based on these data we estimate a biomass for cod of 1.26 × 106 mt in 1852 – compared with less than 5 × 104 mt of total biomass today. In the current policy debate about rebuilding depleted fisheries and restoring marine ecosystems, it is important to recognize that fisheries for key commercial species like cod were far more productive in the past. As we attempt to rebuild these fisheries, our decisions should reflect real and realistic goals for management, not just recently observed catch levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".