Assessing the responses of creek chub (<i>Semotilus atromaculatus</i>) and pearl dace (<i>Semotilus margarita</i>) to metal mine effluents using in situ artificial streams in Sudbury, Ontario, Canada
Bibliographic record
Abstract
Mining of the world's second-largest nickel deposits in the area of Sudbury, Ontario, Canada, has caused acidification and metal saturation of some catchments. We conducted artificial stream studies in the years 2001 and 2002 to assess the effects of treated metal mine effluents (MMEs) from three different mining operations discharging to Junction Creek, Sudbury, on two fish species, creek chub (Semotilus atromaculatus) and pearl dace (Semotilus margarita). Treatments tested for 35 to 41 d included reference water, Garson MME (30%), Nolin MME (20%), and Copper Cliff MME (45%). In 2001, effects on chub included reduced survival and depressed testosterone levels (fivefold reduction) after exposure to all MMEs. In 2002, chub and dace survival were reduced to less than 60% in the Copper Cliff and Garson treatments. In addition, the total body weights of male and female dace were reduced after exposure to the Garson and Copper Cliff treatments. In 2001 and 2002, responses were most common to the 45% Copper Cliff and 30% Garson effluents, with consistent increases in nickel, rubidium, strontium, iron, lithium, thallium, and selenium observed across treatment waters and body tissues. More work is required to link observed effects to field effects and to identify multitrophic level responses of the ecosystem to the MMEs. The artificial stream studies provided a mechanism to identify changes in the endpoints of relevant fish species exposed to present-day metal mine discharges independent of historical depositions of metals in the Sudbury area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".