One Company’s Upstream Water Resources Management Guide
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 a necessity for society, economic development, and the well-being of the environment. Freshwater is not distributed equally around the world, and consequently many regions experience seasonal or longer-term water shortages or excesses. With increasing population and growth in economic development, stress on water supplies is contributing to natural water shortages in some regions. While water use in the petroleum industry is not intensive on a regional basis relative to other users, it can be material at the local scale. Thus water resource management is increasingly recognized as a priority area for global operations. While water issues are often highly location and situation-dependent, our company has developed a standardized guide to water resource management for Upstream oil and gas production projects and operations. The Guide provides environmental, regulatory and socioeconomic (ER&S) practitioners with a consistent and effective methodology to identify, assess and manage water resources-related risks (and opportunities). The Guide has four steps, each with embedded and scalable tools for application by ER&S advisors, local operations advisors, and Corporate subject matter experts; these are: Data Acquisition, Data Analysis, Risk Assessment and Risk Management. Rather than develop an entirely new management system, the Guide was designed to complement existing internal environmental and socioeconomic management and risk assessment/mitigation systems and processes. Application of the Guide is anticipated to result in the enhanced recognition and management of water resources-related risks, decreased capital and operating costs, fewer project and operational delays, improved environmental performance, and a sustained social license to operate.
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