Detecting population regulation of winter flounder from noisy data
Bibliographic record
Abstract
Year-class size of marine fish is thought to be determined during the first year of life, with density-dependent mortality occuring during the larval or juvenile stages. However, investigations of such dynamics are often limited by data availability. To test this paradigm for winter flounder (Pseudopleuronectes americanus) in Narragansett Bay, Rhode Island, the abundances of 29 year classes moving through seven life stages were analyzed with a novel extension of key-factor analysis. Evidence of density dependence was identified between the egg and July young-of-the-year stages and high process-error variance was detected throughout the life cycle, suggesting year-class size is not fully determined until age-2. However, the first summer appeared to be a critical life stage for winter flounder, during which high temperatures, hypoxia, and predator abundance contributed to increased mortality rates behind a long-term population decline. Due to its general data requirements, the key-factor analysis method developed here may be applied to other aquatic populations to identify the impacts of external stressors at particular life stages and the degree to which they are compensated by density-dependent processes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".