Defining the role of omics in assessing ecosystem health: Perspectives from the Canadian environmental monitoring program
Bibliographic record
Abstract
Scientific reviews and studies continue to describe omics technologies as the next generation of tools for environmental monitoring, while cautioning that there are limitations and obstacles to overcome. However, omics has not yet transitioned into national environmental monitoring programs designed to assess ecosystem health. Using the example of the Canadian Environmental Effects Monitoring (EEM) program, the authors describe the steps that would be required for omics technologies to be included in such an established program. These steps include baseline collection of omics endpoints across different species and sites to generate a range of what is biologically normal within a particular ecosystem. Natural individual variability in the omes is not adequately characterized and is often not measured in the field, but is a key component to an environmental monitoring program, to determine the critical effect size or action threshold for management. Omics endpoints must develop a level of standardization, consistency, and rigor that will allow interpretation of the relevance of changes across broader scales. To date, population-level consequences of routinely measured endpoints such as reduced gonad size or intersex in fish is not entirely clear, and the significance of genome-wide molecular, proteome, or metabolic changes on organism or population health is further removed from the levels of ecological change traditionally managed. The present review is not intended to dismiss the idea that omics will play a future role in large-scale environmental monitoring studies, but rather outlines the necessary actions for its inclusion in regulatory monitoring programs focused on assessing ecosystem health.
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".