Detecting gene expression profiles associated with environmental stressors within an ecological context
Bibliographic record
Abstract
Prior to the development of any conservation strategies to mitigate deleterious impacts of environmental change and contamination, there must be a method to make meaningful predictions of the effect of environmental change on organisms. Assessment of relative transcriptomic expression patterns can provide a link between the environment and the physiological response of the organism by identifying genes that respond to environmental stressors; this information could also assist in teasing apart the molecular basis of toxicological effects vs. physiological adaptation. Molecular responses to environmental stressors are probably not restricted to single or few genes, and therefore a more integrative approach is required to examine broad‐scale patterns of transcriptomic response. To address this objective, Chapman et al. (2011) used machine learning tools to link the mechanisms of physiological response to environmental stress; although widely used in clinical applications, such as finding the genetic basis of diseases ( Dybowski & Vanya 2001 ), ecological genomics applications of artificial neural networks are just beginning to emerge. Analyses such as these are important to help identify limitations on the adaptive capacity of organisms and to predict impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".