Commercial harvest of hatchery-reared masu salmon Oncorhynchus masou estimated by a coast-wide sampling program in Hokkaido, northern Japan, and the two-stage sampling schemes of landings
Bibliographic record
Abstract
This paper evaluates the stocking effectiveness of masu salmon Oncorhynchus masou in Hokkaido, northern Japan, through a coast-wide two-stage sampling survey of commercial landings. From January to June 1994-1996, commercial landings of masu salmon at 33-36 fish markets were sampled at 7-10 days intervals, and 60 866-72 124 fish were examined for marks indicated by fin clips. Based on the survey data, numbers of total and hatchery-reared masu salmon landed were estimated. To examine the structure of the errors, stratification of fish markets was implemented on the basis of geography and magnitudes of landings, and the stratification improved accuracy and precision of the estimates. Accuracy of the estimated numbers of total fish was evaluated by being compared to the true numbers of masu salmon landings reported by fishermen's cooperative associations. Estimates of total masu salmon landings were within ± 10% of the true numbers. The estimated recovery rates (± SE) for hatchery-reared masu salmon smolts were variable ranging from 0.18 (± 0.06) to 3.50 (± 0.41)% among the stocked groups. An optimal sampling strategy was examined to obtain precise estimates for future studies.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".