Bioecological traits of benthic macroinvertebrates as alternative tools for ecological risk assessment (ERA): The case of the St. Lawrence River
Bibliographic record
Abstract
The St-Lawrence River is an essential waterway for North America and is exposed to many anthropogenic stresses such as industrial and municipal wastewater or by the agricultural activities. This study is a part of a large research project aiming at developing an ERA tiered framework for sediment management, in the context of integrated management of contaminated sediment, site restoration and sustainable navigation. The purposes of this part of the study are to develop traits approach for the St. Lawrence River and to assess sediment quality by exploring the relationships between chemical contamination and benthic community structure using traits approach. During falls 2004-2005, macroinvertebrates were collected in 59 sites in the St. Lawrence River, especially in its three fluvial lakes and in the harbour zone of Montreal. Organic (PCBs, PAHs, petroleum hydrocarbons), inorganic (As, Cd, Cu, Cr, Hg, Ni, Pb and Zn) contaminants and sediments characteristics (e.g. grain size, metal-binding phases, nutrients) were measured in whole sediment. To build the matrix of traits, we made a bibliographical research at the level of the genus characteristic. The traits were coded by taking into account regional peculiarities of climate and the same ecozone. The relative influence of the chemical contamination and the environmental characteristics on the structure of the bioecological traits was estimated by multivariate analyses. The results of stations clustering according to the traits, significant indicators traits according to the clusters and the relations with the explanatory variables will presented.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".