Vitamin A physiology and its application as a biomarker of contaminant-related toxicity in marine mammals: a review
Bibliographic record
Abstract
In recent decades, marine mammal populations living in highly polluted areas have experienced incidences of low reproductive success, developmental abnormalities and disease outbreaks. In many of these cases, environmental contaminants were suspected as causal or contributing factors. However, demonstrating a mechanistic link between contaminant exposure and effect in marine mammal populations has proven challenging. Consequently, the development and application of relatively noninvasive biomarkers represents a potentially valuable means of monitoring wildlife populations exposed to elevated levels of contaminants. One touted biomarker is vitamin A (retinol), a "dietary hormone" whose metabolites are required for reproduction, growth, development, immune function, vision and epithelial maintenance. Laboratory studies have shown that many contaminants, including polychlorinated biphenyls (PCBs), polychlorinated dibenzo-para-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), can disrupt vitamin A physiology and alter the distribution of its essential metabolites. Field studies suggest that complex environmental mixtures of these chemicals can also interfere with vitamin A dynamics in free-ranging marine mammals and other fish-eating wildlife. However, circulatory retinol, which is the least invasive measurement of vitamin A status, appears to have variable responses to contaminant exposure. In addition, "normal" circulatory retinol levels have not yet been described for most wildlife species, and not enough is known about the natural physiological events that can alter these concentrations. Confounding factors must therefore be characterized before retinoids can be used as an effective indicator of adverse health effects in marine mammals exposed to elevated levels of environmental contaminants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".