Accuracy and precision of the continuous underway fish egg sampler (CUFES) and bongo nets: a comparison of three species of temperate fish
Bibliographic record
Abstract
Abstract We examine the accuracy and precision of the continuous underway fish egg sampler (CUFES) relative to bongo nets based on the catch ratio of Atlantic cod ( Gadus morhua ), American plaice ( Hippoglossoides platessoides ), and cunner ( Tautogolabrus adspersus ). We derived expectation of catch ratios based on the application of a one‐dimensional model of the vertical distribution of fish eggs applied to cod and on prior data on egg vertical distribution. Samples were collected in May and August 2001, two periods when the vertical structure of the water column differed substantially. Stationary CUFES collections did not yield significant differences in accuracy or precision relative to the underway CUFES. In May, when there was relatively little stratification, the CUFES‐to‐bongo catch ratio of cod and plaice eggs was well within expectations based on model predictions. In August, the CUFES‐to‐bongo catch ratios of cod and cunner were higher than expected. Generally, there was a greater proportion of early stage eggs in bongo than in CUFES samples, with the strongest differences in American plaice. The replicate variance of the CUFES was ∼25 times greater than that of the bongo nets, probably because of the large volumes sampled by bongo nets relative to the CUFES. Given that the CUFES provides greater accuracy in mapping but lower precision than bongo nets, multiple sampling gears may be the most effective method for surveying fish eggs of pelagic and demersal species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 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 teacher head, 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".