Correlates of mercury in female river otters ( <i>Lontra canadensis</i> ) from Nova Scotia, Canada
Bibliographic record
Abstract
Mercury (Hg) can reach toxic concentrations in aquatic habitats, sometimes as a consequence of human activity. Mercury can have deleterious effects, particularly in piscivorous mammals in which it bioaccumulates. Furs from trapper-provided female otter (Lontra canadensis) carcasses in Nova Scotia were analyzed for total Hg. Concentrations of total Hg in fur samples averaged 25 µg/g dry weight, ranging from 1.4 to 137 µg/g; 20 µg/g is the fur concentration at which toxic effects are expected. Mercury concentrations were greater in otters from watersheds with bedrock substrates known to contain more available Hg, from otters trapped farther inland, and from otters trapped on watersheds with hydroelectric dams. Otter reproductive potential was measured by counting the number of blastocysts in reproductive tracts. Tooth annuli were used to age otters. Reproductive potential was not related to Hg concentration, nor was Hg concentration related to age. In a general linear model, 53% of variation in fur Hg was explained by underlying bedrock, distance from the coast at which otters were trapped, and presence/absence of a hydroelectric dam. The proportion of juveniles in a population did not differ relative to bedrock Hg concentration, but was lower on watersheds with hydroelectric dams. Because we found no evidence of reduced reproductive potential from greater Hg concentrations, the low proportion of juveniles suggests that Hg reduced juvenile survival, although our evidence is circumstantial.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".