Identification of ecological structure and species relationships along an oceanographic gradient in the Gulf of Maine using multivariate analysis with bootstrapping
Bibliographic record
Abstract
Ecosystem-based fisheries management requires a fundamental understanding of ecosystem boundaries and interspecies interactions. We report a delineation of fish and invertebrate data collected by trawl survey along the Maine, USA, coast. Principal components analysis (PCA) reduced the multidimensionality of the data and created new variables from correlations among species. Bootstrapped PCA was employed to assess PCA structure using eigenvalue variation and species associations using eigenvector variation. A general linear model related structure of fish community identified in PCA to depth, temperature, longitude, and interactions among these variables. Generally, alongshore and onshore–offshore assemblage patterns related to oceanographic gradients, with seasonal variation. PCA-created variables act as indicators of biodiversity and are related to the scale of observation, allowing for multiple scales to be integrated if data are available. Species targeted and gear used along with spatial extent and sampling density must be considered when management zonation is to be undertaken but would allow boundaries to follow biological and physical gradients rather than relying on the typical political and economic variables, avoiding ecologically deleterious spatial patterns in fishing pressure or other instances of de facto zoning. Regardless, this study describes some contributing factors causing division of species assemblages that should be considered.
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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 | 0.002 |
| 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".