Assessing benthic communities’ health by the means of epibenthic indicators in Gulf and Estuary of St.Lawrence, Canada
Bibliographic record
Abstract
Management tools are needed to characterize the increasing numbers and intensities of stressors resulting from human activities that affect marine organisms. This project aims to create epibenthic indicators to qualify the health condition of Estuary and Gulf of St. Lawrence (EGSL) communities that are subjected to multiple anthropogenic stressors. There are many advantages to using marine macroinvertebrates in the development of health indicators. They are closely related to bottom sediments – where contaminants usually accumulate and where oxygen stress is more frequent. Most benthic organisms are also sessile, which means their health condition is a picture of the local environment quality. The aim of this project will be to first test the benthic indicators that have already been developed around the world with the epibenthic communities of the St. Lawrence. Second, we will develop new indicators for stressors that may not be covered by existing indicators for St. Lawrence communities. It is hoped that such indicators of epibenthic health will enable scientists and environmental managers to monitor the condition of the EGSL ecosystem over time.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".