Organohalogens in A Whale-Blubber-Supplemented Diet Affects Hepatic Retinol and Renal Tocopherol Concentrations in Greenland Sled Dogs (<i>Canis familiaris</i>)
Bibliographic record
Abstract
The aim of this study was to examine the plasma, liver, and kidney status of vitamin A (retinol) and vitamin E (alpha-tocopherol) in two groups of Greenland sled dogs (Canis familiaris), with a total number of 16 bitches and 8 pups. The dogs were fed either minke whale (Balaenoptera acuterostrata) blubber (exposed dogs) or uncontaminated (control group) porcine fat for up to 12 to 21 mo of age. The daily intake of 50-200 g whale blubber (mean: 112 g) constituted between 10.4 and 11.7 microg/kg body weight summation operatororganohalogen contaminants (OHC) (or between 4.6 and 6.1 microg/kg body weight summation operatorpolychlorinated biphenyls [PCB]). Retinol was approximately 18% and alpha-tocopherol 22% higher in the diet of the exposed dogs compared to controls. In adipose tissue, mean of SigmaOHC was 92 ng/g lipid weight (lw) and 5005 ng/g lw for all control (n = 12) and exposed dogs (n = 10), respectively. Hepatic retinol correlated negatively with Sigma-dichlorodiphenyldichloroethane (SigmaDDT) and and Sigma-polybrominated diphenyl ethers (SigmaPBDE) for all exposed animals. A negative correlation between kidney alpha-tocopherol and SigmaPCB concentrations was observed, whereas two positive significant correlations were observed between kidney retinol and Sigma-chlordane-related compounds (SigmaCHL) and dieldrin concentrations. Hepatic alpha-tocopherol concentrations were significantly lower in exposed compared to controls, most likely due to a combination by OHC exposure and high dietary intake of unsaturated fatty acids. These results suggest that dietary exposure from OHC may, even at low concentrations, possibly affect retinol and alpha-tocopherol status in Arctic top predators.
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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 | 0.000 | 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".