Omics Advances in Ecotoxicology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Toxic substances in the environment generate adverse effects at all levels of biological organization from the molecular level to community and ecosystem. Given this complexity, it is not surprising that ecotoxicologists have struggled to address the full consequences of toxic substance release at ecosystem level, due to the limits of observational and experimental tools to reveal the changes in deep structure at different levels of organization. -Omics technologies, consisting of genomics and ecogenomics, have the power to reveal, in unprecedented detail, the cellular processes of an individual or biodiversity of a community in response to environmental change with high sample/observation throughput. This represents a historic opportunity to transform the way we study toxic substances in ecosystems, through direct linkage of ecological effects with the systems biology of organisms. Three recent examples of -omics advance in the assessment of toxic substances are explored here: (1) the use of functional genomics in the discovery of novel molecular mechanisms of toxicity of chemicals in the environment; (2) the development of laboratory pipelines of dose-dependent, reduced transcriptomics to support high-throughput chemical testing at the biological pathway level; and (3) the use of eDNA metabarcoding approaches for assessing chemical effects on biological communities in mesocosm experiments and through direct observation in field monitoring. -Omics advances in ecotoxicological studies not only generate new knowledge regarding mechanisms of toxicity and environmental effect, improving the relevance and immediacy of laboratory toxicological assessment, but can provide a wholly new paradigm for ecotoxicology by linking ecological models to mechanism-based, systems biology approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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 it