Using macroinvertebrate functional traits for assessing sediment quality in the St. Lawrence River
Bibliographic record
Abstract
How macroinvertebrate respond to human disturbances can be characterized following two major approaches. The traditional taxonomic approach has been extensively used. Since the nineties, another approach based on functional traits has experienced and offer a better understanding of community-environment relationships and functioning of ecosystems facing human impacts. In this study, we assessed sediment quality in a large river in North America by exploring the relationships between chemical contamination and benthic community structure using the functional trait approach. This study was carried out in the St-Lawrence River, an essential waterway exposed to many anthropogenic stresses such as industrial and municipal wastewater or agricultural activities. Macroinvertebrates were collected in 59 sites. Organic, inorganic contaminants and sediments characteristics (grain size, organic matter, nutrient, etc) were measured in the whole sediment. Seventeen biological or ecological traits of taxa were coded, taking into account regional climate and ecosystem specificities. The goals of this study were: (1) to describe spatial patterns in functional traits of macroinvertebrate communities; (2) to determine relationships between trait combinations and taxonomic structure and (3) to link macroinvertebrate assemblages and trait combinations to environmental conditions and sediment quality.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".