Design and application of a transparent and scalable weight-of-evidence framework: An example from Wabamun Lake, Alberta, Canada
Bibliographic record
Abstract
A weight-of-evidence (WOE) framework was developed to evaluate potential effects on the aquatic ecosystem of Wabamun Lake (Alberta, Canada) associated with the release of Bunker "C" oil after a train derailment. The wide variety of stakeholders and interested regulatory agencies made it necessary to develop a consistent and transparent approach to assessing ecological effects on multiple ecosystem components within the lake with the use of a large number of lines of evidence (LOEs). Consequently, a scalable WOE framework was necessary to integrate the findings of 38 different LOEs. A priori and a posteriori weighting factors were applied to each individual LOE, and a combination of numeric and nonnumeric rating systems was used to integrate LOEs into an overall WOE conclusion for 5 different ecosystem components. We provide guidance regarding the development of a WOE framework and emphasize techniques that enhance the application of best professional judgement during the WOE process.
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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 | 0.162 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".