Predicting the vertical profiles of anchovy (<i>Engraulis mordax</i>) and sardine (<i>Sardinops sagax</i>) eggs in the California Current System
Bibliographic record
Abstract
Abstract Several published models exist for simulating vertical profiles of pelagic fish eggs, but no one has rigorously assessed their capacity to explain observed variability. In this study, we applied a steady‐state model, with four different formulations for vertical diffusivity, to northern anchovy (Engraulis mordax) and Pacific sardine (Sardinops sagax) eggs in the California Current region. Vertical mixing profiles, based on wind speed and hydrography, were combined with estimated terminal ascent velocities of the eggs based on measurements of egg buoyancy and size, to simulate the vertical profiles of the eggs. We evaluated model performance with two data sets: (1) vertically stratified tows for both species and (2) paired samples for sardine eggs from 3‐m depth and in vertically integrated tows. We used two criteria: whether the model predicted individual observed vertical profiles (1) as well as the observed mean and (2) better than the observed mean. Model predictions made with the formulation producing the most gradual profile of vertical diffusivity provided the best match to observations from both data sets and for both species. Addition of a random error term to the terminal ascent velocity further improved prediction for anchovy eggs, but not sardine. For the paired data, model prediction of integrated abundance from abundance at 3‐m depth had significantly lower mean square error than prediction based on a linear regression of 3 m on integrated abundance. Our results support the feasibility of using data from the Continuous Underway Fish Egg Sampler quantitatively as well as qualitatively in stock assessments.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".