Bibliographic record
Abstract
In 1991, the World Business Council for Sustainable Development (WBCSD) introduced “Eco-\nEfficiency” as a management strategy to link financial and environmental performance to create\nmore value with less ecological impact. Based on this strategy, CETAC-WEST (Canadian\nEnvironmental Technology Advancement Corporation - West), in mid-2000, introduced a\npractical approach to eco-efficiency to Western Canada's upstream oil and gas sector. The\nCETAC-WEST Eco-Efficiency Program, focused primarily on sour gas processing facilities, has\ndeveloped methods and programs to identify opportunities for energy conservation and GHG\nreductions. The program outlined in this paper consists of four interrelated phases that are used\nto identify and track efficiency opportunities as well as promote the use of energy efficient\nmethodologies and technologies. If, as program results suggest, 15% to 20% of the gas that is\nnow consumed at by plant operations can be saved through efficiencies, it would save $500 to\n$700 million worth of gas for sale on the market. Although this small Pilot Program in the gas\nprocessing sector has surfaced major opportunities, there are significantly greater opportunities in\nother sectors with high GHG emissions intensity, such as sweet gas processing, conventional oil,\nheavy oil and oil sands. Capturing these opportunities will require a carefully considered strategy.\nThis strategy should include, in addition to commitments for expanding the scope of the current\nProgram, sustained leadership by industry champions and by governments - all aimed at\nchanging the operating mode and improving the culture in the oil and gas industry.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".