Freshwater mussels in an impacted watershed: Influences of pollution from point and non-point sources
Bibliographic record
Abstract
The Grand River watershed in a densely populated region of Ontario supports one of the richest assemblages of freshwater mussels in Canada. However, water quality in this watershed is influenced by urban development, agriculture, and industry. Mussel populations and water chemistry in the lower Grand River and the Boston Creek tributary were evaluated to determine whether point sources of pollution such as discharges of domestic wastewater and industrial effluent, and non-point sources of pollution are affecting mussel distribution and population structure. Semi-quantitative population surveys conducted at 9 study sites identified 20 mussel species, including 3 Species at Risk. Mussel abundance (34-160 mussels/search hour) and species richness indicated that mussel populations in the lower Grand River watershed are continuing to recover from historical lows reported in the 1970s. However, changes in populations at some sites were consistent with altered water chemistry. Most notable was that the three most abundant mussel species in the Boston Creek tributary downstream of a gypsum plant discharge were significantly smaller in length than those upstream of this site. The water chemistry in this habitat was characterized by elevated conductivity (∼2000 μS/cm) and calcium (∼500 mg/L), as well as concentrations of sulfate (∼1000 mg/L) that can be toxic to freshwater mussels. In the Grand River downstream of the confluence with Boston Creek, there tended to be (p > 0.05) fewer mussels (mean 34 ± 20/search h) compared to upstream (mean 67 ± 15/search h) and this corresponded to altered water chemistry, including elevated sulfate (239 mg/L) downstream of the confluence relative to upstream (58 mg/L). These data indicate that chronic exposures to high levels of major ions is likely driving changes to mussel population structure. In addition, the discharges of wash water from a gypsum plant may be impacting sensitive biota in the main stem Grand River well beyond the immediate tributary receiving environment.
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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 | 0.000 | 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".