Comparison between bioecological traits and taxonomy of benthic macroinvertebrates as tools for ecological risk assessment (ERA): The case of the St. Lawrence River
Bibliographic record
Abstract
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 purpose of this study is to assess sediment quality by exploring the relationships between chemical contamination and benthic community structure using classical taxonomy and functional traits approaches. 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. Taxonomy and functional traits of benthic macroinvertebrates in the St. Lawrence River will be used to assess the relative impact of chemical contamination and sediment characteristics applying multivariate analysis and variance partitioning. We will compare the potential of taxonomy and functional traits approaches for assessing sediment quality in comparison with chemical sediment quality guidelines classification. The preliminary results and the implication for the development of an ERA tiered framework will be discussed.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 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".