Ranking ecosystem impacts on Chesapeake Bay blue crab (<i>Callinectes sapidus</i>) using empirical Gaussian Graphical Models
Bibliographic record
Abstract
Moving toward ecosystem-based fisheries management requires integration of biotic and abiotic factors into our understanding of population dynamics. Using blue crab (Callinectes sapidus) in the Chesapeake Bay as a model system, we applied Gaussian Graphical Models (GGMs) to understand the influence of climatic, water quality, and biotic variables on estimates of key indices of blue crab recruitment for 1990–2017. Variables included the North Atlantic Oscillation (NAO), Susquehanna River discharge, wind forcing, hypoxic volume, submerged aquatic vegetation, and the catch per unit effort of striped bass (Morone saxatilis). Direct effects of age‐1+ crabs and summer salinity on recruitment were significant. Phase of the NAO in summer and spring, summer and winter discharge, and hypoxic volume indirectly affected the recruitment. A simulation study showed that GGM model selection achieved nominal coverage and outperformed structural equation modeling (SEM) and Multivariate Adaptive Regression Splines (MARS). GGMs have the potential to improve ecosystem-based management of blue crabs in Chesapeake Bay. Specifically, the approach can be used to examine ecosystem impacts on blue crab productivity and to improve forecasts of blue crab 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".