Effects of recreational and commercial fishing on blue sharks (<i>Prionace glauca</i>) in Atlantic Canada, with inferences on the North Atlantic population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The nominal catch of blue sharks (Prionace glauca) reported for the Canadian Atlantic grossly underestimates the annual catch mortality of about 1000 tonnes (t), making blue sharks the most frequently caught large shark in Canadian waters. Although blue sharks accounted for 99% of all sharks landed at recreational shark fishing tournaments, tournament catches accounted for only 3% of total fishing mortality. Standardized catch rate indices suggested a decline in blue shark abundance of about 5%6%·year 1 since 1995. An increased mortality rate in recent years was suggested by a decline in the median size of blue sharks in the commercial catch. Two independent calculations suggest that North Atlantic catches exceeded 100 000 t, with catch mortalities ranging between 26 000 and 37 000 t. Because tagging studies indicated that blue sharks are highly migratory with a single population in the North Atlantic, the Canadian contribution to overall population mortality accounts for only 2% of the total. The fact that blue shark populations are relatively productive and resilient may help explain their persistence in the face of high international catch mortality and a decline in relative abundance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it