Blood mercury levels and the stopover refueling performance of a long-distance migratory songbird
Bibliographic record
Abstract
I examined the relationship between total mercury (THg) and plasma triglyceride (TRIG; an indicator of body mass change) levels in the blood of migrating Northern Waterthrushes (Parkesia noveboracensis (Gmelin, 1789)) to test the hypothesis that mercury has a negative influence on the stopover refueling rates of migratory birds. THg levels averaged 0.42 ppm and ranged 0.09–2.08 ppm. Model selection indicated that THg was not important for explaining variation in TRIG relative to capture time, body mass, and year. Summed model weights also indicated that THg had low relative importance. Capture time appeared alone in the global best model and had the greatest relative importance. Subsets of birds in the 25th and 75th percentiles of THg level did not have different levels of TRIG. THg in most birds was higher than mean blood levels reported for several other long-distance migrants from the same geographic region, but below the lowest blood level recently determined to cause adverse effects (reduced reproductive success) in a passerine (0.7 ppm). Blood THg levels in this study did not seem to affect foraging efficiency or other attributes of Northern Waterthrushes enough to reduce their stopover refueling rate. Research is needed to identify mercury effect levels for neurological, physiological, and behavioral changes that would impair the migration performance of passerine birds.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".