Arcellaceans (Testate Lobose Amoeba) as Proxies for Arsenic and Heavy Metal Contamination in the Baker Creek Watershed Region, Northwest Territories, Canada
Bibliographic record
Abstract
Arcellaceans (testate lobose amoebae) were examined for 61 sediment surface samples from lakes in the vicinity of the Giant Mine near Yellowknife, Northwest Territories to;(1) quantify the impact of the mine on the Baker Creek Watershed region, (2) determine the utility of arcellaceans as indicators of arsenic and heavy metal contamination and gauge the success of remediation efforts.Several statistical methods, including cluster analysis, Deterended Correspondence Analysis (DCA), and Redundancy Analysis (RDA), were used to quantify the impact of mining activity on the arcellacean assemblages.Cluster analysis revealed five arcellacean assemblages associated with a range of environmental conditions (e.g.polluted, transitional and remediated).Partial RDA results confirm that arsenic has the greatest influence on the arcellacean distribution, explaining 10.7% of the total variance.Stress-indicating species (e.g.Centropyxids) correlate with high arsenic concentrations, while species characteristic of more healthy lake conditions (e.g.Difflugids) dominate sites with significantly lower arsenic concentrations.
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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 | 0.002 |
| 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".