Temporal trends of contaminants in Arctic char (Salvelinus alpinus) from a small lake, southwest Greenland during a warming climate
Bibliographic record
Abstract
Hg and legacy POPs were analysed in muscle tissue of a landlocked Arctic char (Salvelinus alpinus) population from a small lake in southwest Greenland. Hg concentrations were available for six years and OC concentrations for five years during the period 1994 to 2008. For two years, stable isotope values of nitrogen (δ(15)N) were analysed in muscle tissue in order to infer trophic effects. Hg, Σ10-PCB, ΣDDT and trans-nonachlor concentrations (OC on wet weight basis) showed an increase with increasing fish length, which had to be accounted for when assessing temporal trends. δ(15)N values had a narrow range, and there was no indication of increasing δ(15)N values with char length confirming the sampled char did not vary in trophic morphs as often seen in Arctic lakes. Length-adjusted Hg concentrations showed a significantly increasing trend during the period 1994 to 2008, while Σ10-PCB, ΣDDT, ΣHCH and trans-nonachlor showed a significantly decreasing trend when expressed on wet weight, and decreasing but not significantly when expressed on lipid weight. The reason for this was that the lipid content of muscle showed a significantly decreasing trend with time. The sum of mean monthly air temperature during May to August measured at a nearby meteorological station showed an increasing trend during the study period, and changing temperature conditions probably influenced the temporal trend of especially Hg concentrations and the lipid content.
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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 | 0.000 | 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) | 0.000 | 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".