Sustainable Water Management in the Oil and Gas Industry: Use of the WBCSD Global Water Tool to Map Risks
Bibliographic record
Abstract
Abstract This paper describes an innovative new World Business Council for Sustainable Development (WBCSD) offering, the Global Water Tool, and its use by and value to international oil and gas companies in identifying, prioritizing, and managing their portfolio global risks related to water acquisition, use, and disposal. The Global Water Tool is a free and easy-to-use tool for companies to map their industrial and non-industrial sites to view a picture of their water uses and risks relative to water availability in their global operations and supply chains. It was created through collaboration between numerous non-governmental organizations and business water experts including several global oil and gas companies. Organizations operating or wishing to operate in multiple jurisdictions will find this tool invaluable for informing strategic decision making at the corporate and business unit level. Additionally, while water challenges and opportunities occur at the local level, there are advantages for companies in employing a comprehensive, strategic approach to evaluating and addressing water risks across their asset portfolio. Tool metrics and mapping results and implications for global oil and gas companies are described in the paper.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".