A comparison of temporal patterns in the ocean spatial distribution of California's Central Valley Chinook salmon runs
Bibliographic record
Abstract
We developed a broadly applicable method for estimating stock-specific spatial distributions based on patterns in contacts per unit effort determined from data collected in ocean fisheries. The method fully accounts for fishing effort and quantifies uncertainty in total contacts due to sampling error and the effects of annual variability in size-at-age on estimated contacts with sublegal-sized fish. As a case study, we used coded-wire tag recoveries to compare ocean spatial distributions among fish from four return run timings (fall, late-fall, winter, and spring) of Chinook salmon from the Central Valley, California, USA, and explored how distributions varied annually, seasonally, and with fish age in the data-rich fall run. All runs were rarely contacted in ocean fisheries north of Cape Falcon, Oregon (45°46′N). Late-fall and winter run fish appeared relatively restricted to the south compared with fall run fish, corresponding to life history differences and highlighting the ability of spatial management to control impacts on the endangered winter run. For the fall run, the location of highest relative contacts per unit effort of age-3 fish varied across years. This variation correlated with sea surface temperature the previous summer, suggesting ocean distributions may be more responsive to the environment than previously appreciated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".