One Company's in Situ Wildlife Mitigation and Monitoring Program: An Industry-Leading Approach
Bibliographic record
Abstract
Abstract Minimizing and mitigating the environmental impacts of our developments and operations is a very important business driver for Devon Canada Corporation (Devon). Devon takes this responsibility seriously, and believes in maintaining social license to operate, respecting stakeholder interests, and not just meeting, but exceeding, regulatory requirements where it makes sense to do so. Our commitment to the environment is reflected in our corporate policies, which include a Biodiversity and Land Stewardship Policy that provides overarching top-level direction to wildlife, biodiversity and land issues. Wildlife and biodiversity are among the most tangible concerns for stakeholders in Alberta's oil sands region. In 2002, very little was known about wildlife responses to in situ oil sands development. Acknowledging this information deficiency, and recognizing that in situ oil sands development was a long term and incremental commitment, Devon initiated what would become an ongoing In Situ Wildlife Mitigation and Monitoring Program to monitor wildlife populations, conduct research to fill key data gaps, and mitigate negative impacts to wildlife and biodiversity in and around our project areas. To achieve our vision of minimizing impacts to wildlife in the oil sands region, we have made biologically sound commitments, collaborated with peers and other resource sectors, developed strategic research partnerships, and engaged employees at every level throughout the company in implementation. In its current state, the Program has now been endorsed by regulators as the best-in-industry for such initiatives, and has been recognized with numerous awards for its comprehensiveness, innovation and corporate commitment.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".