Applications and implications of neurochemical biomarkers in environmental toxicology
Bibliographic record
Abstract
Thousands of environmental contaminants have neurotoxic properties, but their ecological risk is poorly characterized. Contaminant-associated disruptions to animal behavior and reproduction, both of which are regulated by the nervous system, provide decision makers with compelling evidence of harm, but such apical endpoints are of limited predictive or harm-preventative value. Neurochemical biomarkers, which may be used to indicate subtle changes at the subcellular level, may help overcome these limitations. Neurochemical biomarkers have been used for decades in the human health sciences and are now gaining increased attention in the environmental realm. In the present review, the applications and implications of neurochemical biomarkers to the field of ecotoxicology are discussed. The review provides a brief introduction to neurochemistry, covers neurochemical-based adverse outcome pathways, discusses pertinent strengths and limitations of neurochemical biomarkers, and provides selected examples across invertebrate and vertebrate taxa (worms, bivalves, fish, terrestrial and marine mammals, and birds) to document contaminant-associated neurochemical disruption. With continued research and development, neurochemical biomarkers may increase understanding of the mechanisms that underlie injury to ecological organisms, complement other measures of neurological health, and be integrated into risk assessment practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".