Environmental and biological factors associated with the stage-specific abundance of brown shrimp (<i>Penaeus</i> <i>aztecus</i>) in Louisiana: applying a new combination of statistical techniques to long-term monitoring data
Bibliographic record
Abstract
Several short-term and small-scale correlative studies have associated brown shrimp (Penaeus aztecus) recruitment with high densities of sub-adults, high salinity, warm temperature, low river flow, and low precipitation. In this paper, we address criticisms of traditional correlative studies by using a spatially and temporally extensive dataset, by comparing stepwise multiple regression (SMR) to Bayesian model averaging (BMA), and by investigating nonlinear relationships with generalized additive models (GAMs). We use this combination of statistical methods to examine relationships between annual, stage-specific abundance estimates and environmental factors. BMA and SMR resulted in models with similar explanatory power, but BMA suggested fewer linear predictors. GAMs did not suggest nonlinear relationships among stage-specific abundance estimates. Postlarval abundance was not well described by any model. Juvenile abundance was partially described by environmental variables such as temperature, water clarity, and water level. Adult abundance was well described by early-juvenile abundance, salinity, and temperature. These results suggest that juvenile abundance may be the critical component in determining year-class strength of brown shrimp. Identifying mechanisms that regulate juvenile production within the estuary will be a critical step in effectively managing Louisiana's brown shrimp resource.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".