News | Spheres of Influence ALBERTA’S OIL SANDS
Bibliographic record
Abstract
Pitched battles are a regular occurrence in northern Alberta, Canada, as development of the province’s oil sands continues to expand. One ongoing battle—with another salvo launched in February 2011 with the leak of a European Commission report1 —concerns how dirty oil sands are, relative to other fuels. Another concerns the influence of the oil sands industry in monitoring its own activity. 2 In an effort to cut through the rhetoric of health advocates, industry representatives, environmentalists, government officials, and local residents, the Royal Society of Canada (RSC) selected and covered expenses for an expert panel to winnow out the facts. In a report issued 15 December 20103 the panel cited substantial evidence that efforts to extract oil from the Alberta deposits have degraded air, land, and water quality to varying degrees. The extent of the degradation is sometimes controversial; water quality data, in particular, are subject to differing interpretations and attributions of causality. However, the panel says that, based on publicly available evidence, there appear to be no significant human health threats to the general population either now or from development anticipated in the next decade or so. But the panel also warns that their conclusions come with a major caveat: there are major gaps in health and environmental data, risk assessments, government oversight, information transparency, industry efforts, and disaster preparedness. The health of the region could hinge on these gaps being addressed, particularly since, according to Travis Davies, a spokesman for the Canadian Association of Petroleum Producers, 97 % of projected oil extraction and processing is still to come.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".