Tracking the Recovery of Freshwater Mussel Diversity in Ontario Rivers: Evaluation of a Quadrat-Based Monitoring Protocol
Bibliographic record
Abstract
Watershed inventories and population monitoring are essential components of efforts to conserve and recover freshwater mussel diversity in Canada. We used two datasets to assess the efficacy of a quadrat-based sampling protocol for: (1) detecting mussel species at risk; (2) characterizing species composition; (3) providing accurate estimates of abundance; and (4) detecting changes in density. The protocol is based on a systematic design (with random starts) that samples 20% of monitoring sites with visual-tactile surface searches and excavation of 1 m2 quadrats. The first dataset included 40 sampling sites in five Ontario rivers, and the second dataset consisted of complete census sampling at two 375 m2 sites that represented contrasting mussel assemblages. Our results show that the protocol can be expected to detect the majority of species present at a site and provide accurate and precise estimates of total mussel density. Excavation was essential for detection of small individuals and to accurately estimate abundance. However, the protocol was of limited usefulness for reliable detection of most species at risk. Furthermore, imprecise density estimates precluded detection of all but the most extreme changes in density of most individual species. Meeting monitoring objectives will require either substantially greater sampling effort under the current protocol, or a fundamental revision of the sampling approach.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".