Bryophytes as Heavy Metal Biomonitors in the Canadian High Arctic
Bibliographic record
Abstract
Mosses are a major component of the tundra flora in the Canadian Arctic, yet their use in arctic contaminant research is lacking. Biomonitoring of atmospheric heavy metal deposition using mosses has been extensively employed in Europe, providing a higher sampling density than precipitation monitoring. Temporal, spatial, and habitat gradients of concentrations and enrichment factors of As, Cd, Cr, Cu, Ni, Zn, and Pb (and its stable isotopes) in mosses from Ellesmere Island are examined. Anthropogenically influenced concentrations of As, Cr, Cu, Ni, and Zn in samples collected in 2007 were observed. Concentrations of heavy metals in hydric taxa were larger than those observed in xeric or mesic taxa, though non-significant. Generally, heavy metal concentrations decreased from 1983 to 2007 in a single high arctic locality, though non-significant. Pb-isotope ratios were radiogenic and characteristic of the High Arctic Islands. Trends in high arctic moss data corresponded with environmental proxies such as glacial ice cores, lake sediments, and atmospheric aerosols illustrating the usefulness of bryophytes as biomonitors. This paper outlines the utility of using mosses as biomonitors of heavy metal depositions in the Canadian High Arctic.
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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 | 0.002 | 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) | 0.000 | 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".