Density-dependent regulation of year-class strength in age-0 juvenile striped bass (<i>Morone saxatilis</i>)
Bibliographic record
Abstract
Abundance of age-0 striped bass ( Morone saxatilis ) exhibits 50-fold variability in Chesapeake Bay. Processes that act to reduce and thus regulate this variability were investigated. The potential for density-dependent regulation of growth and mortality in the early juvenile stage and its causes were investigated. Data from multiple seine and trawl surveys in upper Chesapeake Bay and tributaries were analyzed to construct growth and mortality indices having a high degree of spatial and temporal resolution. Age-0 mean lengths in September were inversely related to density, ranging from 67.8 mm in 1994, when mean density was 0.036·m–2, to 104.5 mm in 1992, when mean density was 0.003·m–2. Except for the Potomac River, evidence for density-dependent growth was consistent across subpopulations. Bioenergetics modeling indicated that prey consumption was limiting except in low-abundance years. Mortality increased with respect to abundance and also was density-dependent. The significant interaction between age-0 juvenile length in September and subsequent winter temperature on mortality indicated that density-dependent growth leads to size-selective overwinter mortality. A statistical model including age-0 abundances, age-0 lengths, and winter temperature explained a substantial fraction of variability and the mechanisms for regulation of striped bass recruitment.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".