CAN WE STOP THE ATLANTIC LOBSTER FISHERY GOING THE WAY OF NEWFOUNDLAND’S ATLANTIC COD? A PERSPECTIVE
Bibliographic record
Abstract
The cod and lobster fisheries of Atlantic Canada are managed in verydifferent ways. Regulatory policy for Atlantic cod has traditionally beenbased on population or biomass measurements, something that has neverbeen done for the management of Atlantic Canada’s lobster. While thesetraditional methods differ, an alternate logical or analytic approach tomanagement is perhaps one way that sound and rational fisheries can bemanaged. The recommendations that follow derive from asking: can welearn analytic lessons from the collapse of Atlantic cod that might allow usto avoid a similar collapse in Atlantic lobster? A landings-per-unit-of-effort(LPUE) index could be constructed for the lobster industry that wouldprovide a continuous trend over time. This trend would form an effectivefeedback model; a declining trend over time would indicate the goal ofsustainability was in jeopardy, whereas a level or increasing trend overtime would indicate that the industry was maintaining its sustainability.Crucially, an LPUE index should only be used as an argument a posterioriinvolving feedback in the form of trends. This index should never be usedas an argument a priori to estimate lobster abundance or lobster biomass
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".