Spatial distribution of epifaunal communities in the Hudson Bay system
Bibliographic record
Abstract
The seasonal sea ice cover and the massive influx of river runoff into the Hudson Bay System (HBS) of the Canadian Arctic are critical factors influencing biological production and, ultimately, the dynamics and structure of benthic communities in the region. This study provides the most recent survey of epibenthic communities in Hudson Bay and Hudson Strait and explores their relationships with environmental variables, including mean annual primary production and particulate organic carbon in surface water, bottom oceanographic variables, and substrate type. Epibenthic trawl samples were collected at 46 stations, with a total of 380 epibenthic taxa identified, representing 71% of the estimated taxa within the system. Three communities were defined based on biomass and taxonomic composition. Ordination analyses showed them to be associated primarily with substrate type, salinity, and annual primary production. A first community, associated with coarse substrate, was distributed along the coastlines and near the river mouths. This community was characterized by the lowest density and taxonomic richness and the highest biomass of filter and suspension feeders. A second community, composed mostly of deposit feeders and small abundant epibenthic organisms, was associated with soft substrate and distributed in the deepest waters. A third community, associated with mixed substrate and mostly located near polynyas, was characterized by high diversity and biomass, with no clearly dominant taxon. The overall analysis indicated that bottom salinity and surface-water particulate organic carbon content were the main environmental drivers of these epibenthic community patterns. In the face of climate change, projections of increased river inflow and a longer open water season for the HBS could have major impacts on these epibenthic communities, emphasizing a need to continually improve our ability to evaluate and predict shifts in epibenthic richness and distribution.
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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 | 0.002 | 0.001 |
| 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".