Epibenthic macrofauna community structure of the Gulf of St. Lawrence in relation to environmental factors and commercial fish assemblages: multivariate and geostatistic approaches
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Bottom trawl observations in the northern Gulf of St. Lawrence made by the annual summer survey of the Canadian Department of Fisheries and Oceans give a good opportunity to document the macro-epibenthic invertebrates composition and distribution. This study represents the first attempt to characterize the epibenthic fauna over that wide geographical area. The objective was to establish a relationship between the structure of invertebrate macrofauna communities and fish assemblages and environmental conditions. This relation could highlight critical habitats. In August 2006, 221 bottom trawl stations were surveyed throughout the estuary and the northern Gulf of St. Lawrence. Multivariate and univariate analyses are used to explore the structure and the diversity of the benthic epifauna assemblages (MDS, SIMPER, taxonomic distinctness). Relationships between these assemblages and environmental parameters, such as depth, sediment type, temperature, chlorophyll a, oxygen and bottom currents, are described. About 40% of the macrofauna community structure variance could be explained by the available abiotic factors (Canonical correspondence analysis). General linear models (GLM) were applied to predict the distribution of invertebrates according to significant environmental factors, resulting in a map of benthic habitat. Our findings will help to develop guidelines for adequate conservation measures in the context of integrated marine resource management.
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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 | 0.001 | 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".