The structure of demersal fish assemblages in a cold, highly stratified environment
Bibliographic record
Abstract
Abstract Chouinard, P-M., and Dutil, J-D. 2011. The structure of demersal fish assemblages in a cold, highly stratified environment. – ICES Journal of Marine Science, 68: 1896–1908. Fish are a major component of marine ecosystems, with many species co-occuring in the same habitats. Potential interactions among species and with the environment can be studied through the identification of species assemblages. Data from bottom trawl surveys (2004–2008) conducted in the estuary and northern Gulf of St Lawrence were analysed using multivariate methods (cluster, multidimensional scaling, and detrended canonical correspondence analysis) to describe the structure and composition of demersal fish assemblages, including rare and smaller non-commercial species. The spatial variability in environmental conditions that characterizes the study area has a significant impact on the composition of fish assemblages in the region. In all, 35 taxa were classified as key, and 6 main fish assemblages were described, based on catch in numbers. These assemblages had a coherent spatial distribution in the study area, associated with either depth, salinity and temperature, or dissolved oxygen. The analyses showed overall strong correlations between species abundance and prevalent environmental conditions and explained 18.4% of the variance in species abundance data and 79.2% of the variance in the species–environment relationship.
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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 | 0.001 |
| 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 | 0.001 |
| 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".