Mercury concentrations in landlocked Arctic char (<i>Salvelinus alpinus</i>) from the Canadian Arctic. Part II: Influence of lake biotic and abiotic characteristics on geographic trends in 27 populations
Bibliographic record
Abstract
Among-lake variation in mercury (Hg) concentrations in landlocked Arctic char was examined in 27 char populations from remote lakes across the Canadian Arctic. A total of 520 landlocked Arctic char were collected from 27 lakes, as well as sediments and surface water from a subset of lakes in 1999, 2002, and 2005 to 2007. Size, length, age, and trophic position (delta(15)N) of individual char were determined and relationships with total Hg (THg) concentrations investigated, to identify a common covariate for adjustment using analysis of covariance (ANCOVA). A subset of 216 char from 24 populations was used for spatial comparison, after length-adjustment. The influence of trophic position and food web length and abiotic characteristics such as location, geomorphology, lake area, catchment area, catchment-to-lake area ratio of the lakes on adjusted THg concentrations in char muscle tissue were then evaluated. Arctic char from Amituk Lake (Cornwallis Island) had the highest Hg concentrations (1.31 microg/g wet wt), while Tessisoak Lake (Labrador, 0.07 microg/g wet wt) had the lowest. Concentrations of THg were positively correlated with size, delta(15)N, and age, respectively, in 88, 71, and 58% of 24 char populations. Length and delta(15)N were correlated in 67% of 24 char populations. Food chain length did not explain the differences in length-adjusted THg concentrations in char. No relationships between adjusted THg concentrations in char and latitude or longitude were found, however, THg concentrations in char showed a positive correlation with catchment-to-lake area ratio. Furthermore, we conclude that inputs from the surrounding environment may influence THg concentrations, and will ultimately affect THg concentrations in char as a result of predicted climate-driven changes that may occur in Arctic lake watersheds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".