Environmental factors influencing macrobenthic biodiversity and bioturbation in the Estuary and Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Many marine habitats are presently under pressure by multiple anthropogenic threats. Changes in environmental parameters near the seabed, such as a decrease in the oxygen concentration or an increase in the organic carbon loading, can have negative impacts on biological activity and diversity. Studies have shown that such changes are now taking place in the Estuary and Gulf of St. Lawrence (EGSL). We photographed the seabed at 11 stations in the EGSL during the summers of 2006 and 2007 and analysed 162 images to identify surface manifestations (traces) of bioturbation and identify the macrobenthic organisms that were present. The objectives of this study were 1) to determine the environmental factors influencing the macrobenthic biodiversity and the abundance and diversity of bioturbation traces (or lebensspuren) in the EGSL, and 2) to determine if there is a significant difference between stations with high, medium and low oxygen saturation. Our results show an increase of the area covered by total traces and by surface traces as the oxygen decreases. We also found high densities of Ophiura sp. in hypoxic areas of the EGSL. This could indicate a gradual replacement of hypoxic non tolerant species, responsible for 2 most relief traces, by hypoxic tolerant species, responsible for most surface traces, as Ophiura sp
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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 | 0.002 |
| Science and technology studies | 0.002 | 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".