Bibliographic record
Abstract
Abstract By 2030, the world may face 40 percent water shortfall as a result of increasing population along with impacts from climate change. This results in rising effort from worldwide companies to fully understand current and future risks including liabilities from water use for their operations. In response to this growing concern, PTTEP has conducted a project to establish corporate system to assess future potential water related risks on production assets. Boundary of study covers eleven assets located in five countries (Thailand, Oman, Australia, Canada, and Algeria). Innovative risk assessment is conducted by using both primary information from PTTEP representatives of each asset and secondary data from three worldwide accepted softwares, comprising WBCSD/IPIECA Global Water Tool, WRI Aqueduct Water Risk Atlas, and WWF Water Risk Filter. Since each software presents its unique set of risk indicators, combining all risk results generated by these tools produces higher level of confidence and more accurate results. The system is then capable to undertake scenario analysis to quantify impacts of the pre-defined risk events. The results indicate that, it is not expecting to have significant risk at PTTEP’s assets in Thailand, Oman, Australia, and Canada. However, a major concern for Algeria assets regarding water stress issue is defined. Algeria is also assessed to pose regulatory risks, particularly in the areas of clarity and enforcement of water-related legal framework. Accordingly, it is vital for PTTEP to be aware and monitor changes in government and public perceptions on water related issues in Algeria. The outcome of this project is regarded as an innovation to assist PTTEP to pinpoint water-related risks and quantify the magnitude of impacts if the risks occur. The water related risk assessment tool functions like a compass for PTTEP to develop corporate water management plan and define suitable practices for company’s water footprint reduction.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".