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.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".