Optimal estimation of catch by the continuous underway fish egg sampler based on a model of the vertical distribution of American plaice (Hippoglossoides platessoides) eggs
Bibliographic record
Abstract
Abstract Pepin, P., Curtis, K.A., Snelgrove, P.V.R., de Young, B., and Helbig, J.A. 2007. Optimal estimation of catch by the continous underway fish egg sampler based on a model of the vertical distribution of American plaice (Hippoglossoides platessoides) eggs – ICES Journal of Marine Science, 64, 18–30. We investigate how the vertical stratification of the water column (specifically density) affects predictions of the catch of American plaice eggs (Hipploglossoides platessoides) from a fixed-depth sampler [the continuous underway fish egg sampler (CUFES)] relative to the integrated abundance in the water column measured in bongo tows. A steady-state model of the vertical distribution of fish eggs coupled with a simple model of the vertical profile of eddy diffusivity (i.e. mixing) is applied. Key model parameters are estimated through optimization of a one-to-one relationship between predicted and observed catches fit, using a generalized linear model with a Poisson, negative binomial, or gamma error structure. The incorporation of data on the vertical structure of the water column significantly improved the ability to forecast CUFES catches when using Poisson or negative binomial error structure, but not using a gamma distribution. Optimal maximum likelihood parameter estimates for eddy diffusivity and egg buoyancy fell within the range of expected values. The degree of uncertainty in the parameterization of eddy diffusivity suggests, however, that greater understanding of the forces that determine the vertical profile of mixing is critical to achieving strong predictive capabilities. The inverse problem of predicting integrated abundance from CUFES catches did not benefit from the environmental-driven model because of the high uncertainty in the catches from the CUFES.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".