Integrating Omic Technologies into Aquatic Ecological Risk Assessment and Environmental Monitoring: Hurdles, Achievements, and Future Outlook
Bibliographic record
Abstract
BACKGROUND: In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the U.K. Natural Environment Research Council (Fish Toxicogenomics-Moving into Regulation and Monitoring, held 21-23 April 2008 at the Pacific Environmental Science Centre, Vancouver, BC, Canada). OBJECTIVES: The consortium from government agencies, academia, and industry addressed three topics: progress in ecotoxicogenomics, regulatory perspectives on roadblocks for practical implementation of toxicogenomics into risk assessment, and dealing with variability in data sets. DISCUSSION: Participants noted that examples of successful application of omic technologies have been identified, but critical studies are needed to relate molecular changes to ecological adverse outcome. Participants made recommendations for the management of technical and biological variation. They also stressed the need for enhanced interdisciplinary training and communication as well as considerable investment into the generation and curation of appropriate reference omic data. CONCLUSIONS: The participants concluded that, although there are hurdles to pass on the road to regulatory acceptance, omics technologies are already useful for elucidating modes of action of toxicants and can contribute to the risk assessment process as part of a weight-of-evidence approach.
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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 | 0.079 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| 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".