Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Water is essential in oil and gas operations. Yet water, particularly fresh water, is a scarce resource in many parts of world now and availability in some regions is predicted to become more constrained in the future. This paper will address industry understanding of water risks and impacts and share examples of water management strategies that are integral to sustainable and efficient operations in the sector. Utilizing several publically available tools for improving understanding and evaluating risk, ConocoPhillips has continued to advance both our internal understanding of our risks and mitigation plans and contributed to raising the industry and stakeholder awareness of water management risks in the sector. Through our work with IPIECA, GEMI (Global Environmental Management Initiative) and other organizations, this paper showcases the evolution of the industry’s understanding, tools and guidance developed to better manage water risks, impacts and adaptation. As co-leader in the development of these tools, and from experience in their internal implementation, ConocoPhillips will share the evolution of the topic and the impact the following tools had in managing risks: The IPIECA Global Water Tool for Oil and Gas, customized in collaboration with the World Business Council for Sustainable Development (WBCSD), gives an overview and visual output of water use and risks for a global portfolio across the oil and gas value chain.The GEMI Local Water Tool for Oil and Gas provides a local-level understanding of water risk and development of asset-specific management plans.Development of a focused internal water strategy and areas of emphasis (supported by the IPIECA and GEMI tools) is expanding our transparency and internal understanding of water risks. The paper will share practical implementation results from utilizing these tools, support implementation of good management practices and environmental and operational performance.
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.
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) | 0.001 | 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 it