Variation in the catchability of yellow perch (Perca flavescens) in the fisheries of Lake Erie using a Bayesian error-in-variable approach
Bibliographic record
Abstract
Abstract Catch per unit effort (cpue) from fisheries, and abundance or biomass indices from fishery-independent surveys are often used to infer the dynamics of exploited populations. To do this, cpues and survey indices are usually assumed to be proportional to population size or biomass. Four sources of data on the cpue of yellow perch (Perca flavescens) in Lake Erie were available to evaluate this assumption: commercial gillnet and trapnet fisheries, an angling fishery, and a fishery-independent gillnet survey. The relationships between fisheries cpue and population biomass (estimated from an age-structured model), and between fisheries and survey cpues were analysed by error-in-variable (EIV) models because of the absence of independent estimates of population size. Cpues were not proportional to population size, estimated by biomass. Catchabilities varied widely among fisheries (gear types), time period, and areas (management units) within Lake Erie. A spatial EIV model showed that the migrations among management units were considerable. The whole-lake spatial EIV model showed that cpues were not proportional to population size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".