Tagging of Pacific herring <i>Clupea pallasi</i> from 19361992: a review with comments on homing, geographic fidelity, and straying
Bibliographic record
Abstract
Nearly 1.6 million tagged herring (Clupea pallasi) were released in two separate programs (19361967 and 19791992) in British Columbia. Several thousand tags were released in each of 955 release sessions. Over 85% of the release sessions had subsequent recoveries. Almost 43 000 tags were recovered over all years. We re-assembled the tagging data into an electronic database, geo-referenced all tag release and recovery data, analysed spatial movements, and estimated straying and fidelity rates. The analyses do not wholly support the conclusions of previous work indicating high homing rates to local coastal areas. Estimates of fidelity, defined as the proportion of tags recovered in the same area as released, varied with the size of the geographic area used in the analyses. Fidelity rates are high for large areas, such as the Strait of Georgia (~10 000 km 2 ), but lower for small geographical areas, such as inlets or bays (~100 km 2 ). High fidelity is not necessarily evidence for "homing." Homing and fidelity are different biological processes and tagging cannot necessarily distinguish between them. Although fidelity rates for small areas are generally low, there are exceptions that may be evidence for the existence of biologically distinct populations in certain areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".