Trace Elements in Lesser Scaup (<i>Aythya affinis</i>) from the Mississippi Flyway
Bibliographic record
Abstract
Previous research reported that concentrations of selenium in the livers of 88-95% of lesser scaup from locations in Lake Erie, Lake St. Clair, and Lake Michigan, USA were either elevated (10-33 μg/g dry weight [dw]) or in the potentially harmful range (>33 μg/g dw). In order to determine the geographic extent of these high selenium concentrations, we collected lesser scaup in Louisiana, Arkansas, Illinois, Minnesota, Wisconsin, and Manitoba and analyzed the livers for 19 trace elements. We found that all trace element concentrations, except for selenium, generally were low. Arsenic, which usually is not detected in liver samples, was detected in Louisiana and may be related to past agricultural usages. Chromium, which also is not usually detected, was only present in lesser scaup from Arkansas and may be related to fertilizer applications. Cadmium and mercury concentrations did not differ among locations and concentrations were low. Selenium concentrations in Arkansas (geometric mean = 4.2 μg/g dw) were significantly lower than those in Louisiana (10.7 μg/g dw), Illinois (10.5 μg/g dw), and Minnesota (8.0 μg/g dw); concentrations in Wisconsin and Manitoba were intermediate (6.6 and 6.5 μg/g dw). About 25% of lesser scaup livers contained elevated selenium concentrations; however, none were in the harmful range. We concluded that selenium concentrations in lesser scaup in the Mississippi Flyway are elevated in some individuals, but not to the extent that has been documented in the industrial portions of the Great Lakes.
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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 | 0.001 | 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".