The IPIECA Water Management Framework
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, particularly fresh water, is a scarce resource in many parts of world and further constraints are predicted. Developing and implementing water management practices across the oil and gas lifecycle is therefore considered an essential component in a company’s sustainability strategy. IPIECA, the global oil and gas industry association for environmental and social issues, has recently developed a framework for water management. Adoption of this framework helps align member companies with IPIECA’s goal of the oil and gas industry being recognized by as proactively and collaboratively managing water use and acting as responsible stewards of this resource. The IPIECA framework is also aligned with the goal of the 6th World Water Forum Target 6, which is specifically reated to water management in the oil and gas sector. Acheivement of this target is being led by IPIECA with support from International Association of Oil & Gas Producers (OGP). Since 2010, IPIECA has made significant strides to raise members, stakeholders and the oil and gas industry’s awareness of water management issues including development of the IPIECA Global Water Tool for Oil and Gas and the Global Environmental Management Initiative (GEMI) Local Water Tool for Oil and Gas. The framework builds on this work and will ultimately include a series of industry guidelines, tools and initiatives providing a comprehensive approach to water management through the life of oil and gas development and production. As part of the framework, new guidance is being launched to coincide with the SPE International HSE Conference at Long Beach, California. This paper describes the framework, its aims and the concept as well as introducing the new guidances on "Identifying and Assessing Water Sources " and, "Optimising Water Use through Efficiency ",
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) | 0.000 | 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 it