Balancing environmental, societal and energy production issues*
Bibliographic record
Abstract
Clean burning natural gas begins with environmentally-friendly drilling and production. The industry has made great strides in protecting the environment while increasing production, yet producers still face challenges in relation to effectively operating in environmentally-sensitive areas. The Environmentally Friendly Drilling Program integrates technologies—including: rig designs, drilling fluid systems, waste management, roads and pads—into systems that reduce the impact in environmentally-sensitive areas. The objective is to identify, develop and transfer critical, cost effective, new technologies, which provide policy makers and industry with the ability to develop US domestic reserves in a safe and environmentally-friendly manner. The program was honoured with the Environmental Partnership Chairman’s Stewardship Award from the Interstate Oil and Gas Compact Commission at its 2009 annual meeting. The program, funded by industry and government, provides a comprehensive technology transfer effort, which includes outreach to industry, government and the general public. In addition, a scorecard system is being developed to recognise companies that use the most appropriate technologies and systems to minimise the environmental tradeoffs of operations in sensitive ecosystems. The scorecard assesses drilling operations and technologies with respect to: air, site, water, waste management, biodiversity and societal issues. The goal of the scorecard is to develop a mindset in the industry that environmental stewardship is a core value. In addition, the scorecard enables all stakeholders to understand the balance between energy development and the impact on the environment. The program has made significant advances in reducing environmental tradeoffs and in addressing societal issues associated with natural gas production.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".