Cumulative effects management and the role of predictive models in the new policy for regulating land disturbance in Alberta
Bibliographic record
Abstract
This contribution describes the recent evolution from sector-based environmental management to cumulative effects management (CEM) in Alberta under a new policy direction titled the Alberta Land-use Framework. This paper focuses on the role of models and hypothesis testing in a CEM and performance assurance framework rather than a critique of CEM itself. Two opposing approaches are currently pursued in Alberta to establish the metrics of CEM. Both are outlined and a case is made for reliance on ecologically defined criteria over the potentially dangerous approach of setting socially desired outcomes first. The challenges Alberta regulators face that must be overcome for an ecosystem approach are described under four categories: (1) model suitability, (2) data availability, (3) science representation, and (4) suitability for a regulatory framework. Two case studies are presented that exemplify the success of following an ecosystem modelling approach to CEM and the pitfalls that can occur if the wrong indicators (i.e., ecological end-points that are insensitive to the disturbance) are selected in the CEM approach.
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 | 0.001 | 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".