Practical Guidance on Biodiversity Risk Assessments
Bibliographic record
Abstract
Abstract Biodiversity and Ecosystems Services are critical in maintaining ecosystem health and human well-being and protecting plant and animal species and ecosystems where we operate is an essential component of our health, safety, and environmental commitment. This paper will show the parallel evolution of the industry understanding and the corresponding development of the ConocoPhillips’ biodiversity position, commitments, strategy and actions designed to conserve biodiversity as part of our commitment to reduce the effects of our activities on the environment. The discussion will highlight some key business results, business practices, processes and tools we use to assess impacts, track plans and implement our Biodiversity Position. The paper will cover Development of Biodiversity Action Plans for operated assets and projects located in areas of high conservation value.Use of widely available and effective planning tools such as those developed by lPlECA, Energy and Biodiversity Initiative, and the International Association of Oil and Gas Producers to facilitate biodiversity conservation.Consideration of targeted opportunities for habitat improvement, including projects for rehabilitation.Collaboration with key stakeholders to increase capacity for biodiversity protection, internally and in related institutions and communities. This presentation will show how the company works individually and through industry consortiums to build awareness and responsiveness to this issue.
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 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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.135 | 0.060 |
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".