Metals, metalloids and metallothionein in tissues of fish\nfrom a Canadian freshwater system receiving gold mining\neffluents
Bibliographic record
Abstract
\nWhite suckers (Catostomus commersoni) and walleye (Stizostedion vitreum) were captured\nfrom Balmer Lake, a shallow freshwater system in Central Canada that has served as the final repository\nfor tailings from two gold mines for more than 40 years and from nearby reference locations.\nConcentrations of As, Se, Hg, Cd, Cu, Ni, Pb and Zn were measured in liver, kidney and gill tissues.\nEnrichments of several metals were identified in the fish captured from Balmer Lake relative to the\nreference sites. Concentrations of the metal binding protein, metallothionein, were also measured in liver\ntissue of fish from Balmer Lake and the reference locations in order to examine relationships between\nmetallothionein concentrations and any of the analyzed metals. Data will also be presented for metals and\nmetallothionein in viscera of small bodied forage fish. These data have been collected from several\nexperiments in which forage fish were caged for brief periods at reference sites, within Balmer Lake, and\nat several sites downstream from the release of Balmer Lake waters. The results of these studies show the\npotential for accumulation of several metals/metalloids in fish exposed to effluent from gold mining.\nThey also demonstrate the potential for metallothionein to be used as an indicator of long and short term\nexposure to metals.\n
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".