Gill-Net Saturation in Lake Erie: Effects of Soak Time and Fish Accumulation on Catch per Unit Effort of Walleye and Yellow Perch
Bibliographic record
Abstract
Abstract Gill-net saturation was analyzed through a delta model (i.e., two-stage model) by examining the effects of soak time and fish accumulation (number of fish of all species enmeshed per square meter of a given gill net, including the species of interest) on catch per unit effort (CPUE) of walleyes Sander vitreus and yellow perch Perca flavescens in Lake Erie. The analysis was based on fishery-independent survey data for 1989–2003. In the delta model, the positive values of CPUE were estimated by a generalized additive model (GAM) assuming a log-gamma distribution, and the probability of obtaining nonzero values of CPUE was estimated by a GAM assuming a binomial distribution. Soak time and fish accumulation had significant effects on CPUE. The CPUE of walleyes decreased in gill nets soaked for 10 h and started to decline when fish accumulation was around 2 fish/m2. We did not observe a substantial decline in the CPUE of yellow perch within the soak time interval we examined, but we did observe a decline when fish accumulation was 6–8 fish/m2. The decline in CPUE with increasing soak time for walleyes and with increasing fish accumulation levels for both walleyes and yellow perch indicates that gill-net saturation did exist in Lake Erie gill-net surveys for these two species and that the gill nets were saturated faster by walleyes than by yellow perch. We suggest that gill-net saturation be considered when applying CPUE from gill-net surveys to stock assessment and that the generalized linear additive-based modeling approach be considered as an alternative in gill-net saturation analyses. Received March 11, 2010; accepted January 6, 2011
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".