Trace elements in freshwater ecosystems in the Canadian Arctic
Bibliographic record
Abstract
\nThe expedition Tundra North West 99 visited 17 sites across the Canadian Arctic in order to\nsample freshwater, sediments, soil and biotic compartments of the ecosystems. Trace metal\nconcentrations were determined using ICP-MS. Dating of sediment profiles using $^137$Cs showed a very low sedimentation rate in these lakes ($<$1 mm/yr). The lake waters were mostly well buffered with high pH and hardness, with the exception of lakes on Ellef Ringnes Island and Baffin Island. Trace metal\nconcentrations in lake waters were generally low, but with some locally elevated concentrations. Trace metal profiles in sediments showed influence of catchment geology, indicated by elevated\nconcentrations at some sites, e.g. NW Yukon. Increased concentrations of Cd, Hg, Zn, Pb, Tl and some\nother elements, found in recent sediment layers may indicate long-range airbome pollution. The elevated concentrations of e.g. Cd and Cu in the sediments and water at the Yukon site were also reflected in fish.\nCd-concentrations in Arctic char liver were about 10 times higher than in other areas in the Arctic and in\nnorthern Sweden. Cd-concentrations in char liver were otherwise low with slightly elevated values in the more soft water lake at Baffin Island. Mercury concentrations in sediment at the Yukon site were 20–40 times higher than in the other lakes, whereas the concentration in fish muscle were still not elevated,\nwhich may be explained by the also elevated Se concentrations in the lake. The variations in Hgconcentrations\nin Arctic char were otherwise small, with the highest values at Ungava Peninsula and\nEllesmere Island.\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 | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".