Data Interpretation Issues for Canada's Environmental Effects Monitoring Program
Bibliographic record
Abstract
Abstract As part of Canada's National Environmental Effects Monitoring (EEM) Program, regulated pulp and paper mills are (and metal mines will be) required to submit an interpretive report describing monitoring results. General guidance has been prepared on how to interpret these EEM data—specifically: 1) which effect endpoints to use, 2) the statistical (or other) approach to use for each endpoint to determine the presence or absence of an effect associated with exposure, and 3) the role of power analysis, α, β, and effect size in determining effects. A statistically significant difference (relative to reference conditions) in any of the effect endpoints is to be considered an exposure-associated effect for the purposes of warranting possible follow-up action. Such an effect does not, however, necessarily indicate ecological, social, or economic significance sufficient to require corrective action. Power analyses should be conducted both at the beginning of a study to determine required sampling effort and at the end of a study to determine whether the power that was actually achieved was sufficient to detect the effect size of interest. A key recommendation is to set α = β as a starting point for data interpretation. The initial recommendations of the general guidance are expected to evolve as environmental effects become better understood.
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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 | 0.339 | 0.595 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".