Bibliographic record
Abstract
Fossil fuels--coal, oil, and natural gas--account for about 90% of global energy use (Botkin and Keller 2011). Crude oil--a complex mixture of hydrocarbons--is refined into gasoline, heating oil, asphalt, plastics, kerosene, and other products. Some analysts believe that peak cheap (when Earth's supply of reasonably priced oil runs out) is only a couple of decades away (see On the web). Consequently, more countries are pursuing unconventional sources of oil, including tar sands and oil shale. Oil shale is a type of sedimentary rock containing kerogen, which yields oil when heated. Tar sands (or oil sands) contain bitumen, which yields oil when mixed with hot water. Many analysts believe these unconventional sources of oil will buy us time (see On the web). Let's look more closely at tar sands. Tar sands The United States Department of Energy provides basic background information about tar sands (see On the web). Tar sands are very unevenly distributed around the planet, with about 75% of known deposits near Alberta, Canada. These deposits provide about 10% of North American oil production (Botkin and Keller 2011), and that percentage is expected to rise. Pipelines help move this oil to the United States. As demand for the oil grows, controversy abounds about the possible economic and environmental effects of further development of Canadian tar sands. Keystone XL pipeline TransCanada, a major Canadian energy company, has been working for almost five years to get approval to build an extension to its pipeline system to carry oil from the tar sands region of Alberta to multiple locations in the United States. The Keystone XL Pipeline, as the extension is known, has faced opposition from both American refineries and environmentalists. Recently, despite a rerouting of the proposed line, President Obama postponed any final decision on approval of the pipeline extension until 2013 (see On the web). Classroom activities You'll find a plethora of educational resources from Canada, the global tar sand leader. Various Canadian schools have projects and activities to help students understand the tar sand industry, from the players involved to the potential environmental impacts. The University of British Columbia has a take-home experiment in which students can explore the physics of tar sands by analyzing various methods of separating canola oil and water (see On the web). …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.012 |
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".