Determining the Ecological Status of Benthic Coastal Communities: A Case in an Anthropized Sub-Arctic Area
Bibliographic record
Abstract
With the widespread influence of human activities on marine ecosystems, evaluation of ecological status provides valuable information for conservation initiatives and sustainable development. To this end, many environmental indicators have been developed worldwide and there is a growing need to evaluate their performance by calculating ecological status in a wide range of ecosystems at multiple spatial and temporal scales. This study calculated and contrasted sixteen indicators of ecological status from three methodological categories: abundance measures, diversity parameters and characteristic species. This selection was applied to coastal benthic ecosystems at Sept-Îles (Québec, Canada), an important industrial harbor area in the Gulf of St. Lawrence, and related to habitat parameters (organic matter, grain size fractions, and heavy metal concentrations). Nearly all indicators highlighted a generally good ecological status in the study area, where communities presented an unperturbed profile with high taxa and functional diversities and without the dominance of opportunistic taxa. Some correlations with habitat parameters were detected, especially with heavy metals, and bootstrap analyses indicated quite robust results. This study provides valuable information on the application of environmental indicators in Canadian coastal ecosystems, along with insights on their use for environmental assessments.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".