In search of effective bioassessment of urban stormwater pond sediments: enhancing the ‘sediment quality triad’ approach with oligochaete metrics
Bibliographic record
Abstract
Stormwater ponds have been widely used to control increased volumes and rates of surface runoff resulting from urbanization. As receiving waters, they are under the influence of intermittent pollution from urban wet-weather discharges. Meanwhile they offer new aquatic habitats balancing the transformation of initial ecosystems and their associated biodiversity. Bioassessment of stormwater facilities is therefore crucial to insure the preservation and rehabilitation of biodiversity in urban areas. Nonetheless, the application of traditional bioassessment methodologies such as the sediment quality triad (SQT), based on the comparisons with reference sites, is challenged by the artificial and atypical features of urban stormwater ponds. Our concern in finding a more specific and effective bioassessment methodology led us to consider associating the Oligochaete Index Methodology (OIM) with the SQT. This study shows that although some adjustments were needed, the OIM brought new and complementary information to the SQT assessment on the effects of contaminants and on the biological quality status of the sediment in a test urban stormwater pond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".