A Comparison of Two Methodologies for Estimating Brook Trout Catch and Harvest Rates using Incomplete and Complete Fishing Trips
Bibliographic record
Abstract
Abstract On the island portion of Newfoundland and Labrador, Canada, the provincial government relies on roving creel surveys to assess the fishery for brook trout Salvelinus fontinalis. The estimation of catch and harvest rates for these surveys requires on-site interview methods that gather information from incomplete fishing trips. When the objective is to determine total catch, the mean-of-ratios estimator is the accepted method for deriving catch rate from incomplete trips, whereas the ratio-of-means estimator is the accepted method for deriving catch rate for completed trips. When we compared the two estimators using incomplete and complete trip catch data measured from the same sample of anglers, we found a persistent bias. Catch and harvest rates derived from the mean-of-ratios estimator were significantly higher than those obtained with the ratio-of-means estimator. Catch rate was higher by 32%, while harvest rate was higher by 39%. When we only used the completed trip data set for both calculations, we found a mean difference of 19% between the two estimators. We also found a second source of error. When we examined individual angler responses for catch and harvest, the estimates revealed a positive bias whereby the incomplete trips showed higher estimates relative to the complete trips (16% and 21%, respectively). These biases appear to be related to the estimation procedure as well as fish and fisher behavior at the individual angler level. We use linear regression analysis to help correct the bias associated with the mean-of-ratios estimator.
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".