Patterns of diversity in stream macroinvertebrate communities in the Sydenham River watershed (SW Ontario)
Bibliographic record
Abstract
Stream macroinvertebrates are commonly employed bioindicators for stream assessments, as their tolerances to pollution and other stressors are well studied. However, invertebrate diversity metrics might assess streams at different, often finer scales than that of human disturbance, especially in agriculture landscapes. Our research seeks to better understand how water quality, sediment and land-use impacts stream biodiversity in the Sydenham River watershed, Southwestern Ontario. More specifically, this study asks: does macroinvertebrate diversity vary predictably with environmental factors at the microhabitat or reach scales? Here we present biodiversity data from a watershed survey of the Sydenham River and its tributaries conducted in Fall of 2020. Invertebrates were sampled at randomly selected and existing monitoring sites following Ontario Benthic Biomonitoring Network protocols. Water quality, sediment and land-use data were collected using a modified Ontario Stream Assessment Protocol. Data were analysed using a suite of exploratory statistics, including ordination and other community analyses, to determine which factors best explained variance in invertebrate richness and biodiversity indices across microhabitat and reach scales. Watershed assessment and restoration face challenges in overcoming barriers of scale as while restoration actions take place locally, previous publications suggest that coordinated actions across a landscape may be required to ensure ecosystem recovery. The findings of this research have the potential to inform local understanding of ecosystem health and help accelerate more coordinated restoration and stewardship practices in the region.
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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 | 0.001 | 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".