Arsenic Bioconcentration in Freshwater Fish Species in a Pristine Lake in Yellowknife, NT
Bibliographic record
Abstract
Yellowknife hosts one of the largest gold mining industries in Canada, including the renowned Giant Mine actively operating from 1948 to 2004, The mining operation in Giant Mine released enormous amounts of arsenic trioxide dust to the environment from the burning of arsenopyrite ores necessary to extract gold. Studies have revealed up to 100-fold increases in the concentration of arsenic in the surface water of lakes located within a 5-km radius of the mine. We summarized different factors from previous studies that contribute to the high arsenic levels found in lakes in Yellowknife: distance from the Giant Mine, prevailing wind direction to the Northwest, and the size of the lake. In this study, Small Lake was chosen as a pristine lake; a medium-sized lake located 27-km East of the mine and away from the city centre. Small Lake has the background concentration of arsenic of 1.4 µg/L in its surface water, unaffected by the historical activities from the Giant Mine. This background level of arsenic is expected from the slow natural weathering process of the bedrock geology. We collected two most common freshwater fish species in lakes in Yellowknife: adult Lake Whitefish (Coregonus clupeaformis) (n=8) and adult Northern Pike (Esox lucius) (n=8). Total arsenic concentration in the tissues was measured using ICP-MS method following FDA standard. Results show arsenic concentrations of 0.567 ± 0.216 mg/kg dry weight in the muscle tissue of Lake Whitefish and 0.458 ± 0.115 mg/kg dry weight in the muscle of Northern Pike. Although the Northern Pike species in this study is in a higher trophic position than the Lake Whitefish species, no significant difference in the arsenic concentration is observed in the muscle tissues (p > 0.05). The results of this study serve as reference data for fish arsenic monitoring programs and risk assessment projects in Yellowknife.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".