Bibliographic record
Abstract
What most Americans don't know is that for the last seven years, Canada has been the number one supplier of oil to the United States. . . .They don't know that a forest the size of Florida will be industrialized by this operation.They have no idea that it will be coming from some of the world's largest open pit mines.They have no idea that it's coming from operations that are creating three times more carbon emissions than conventional oil.And although a lot of Americans would find it comforting to know that they are no longer dependent on hostile suppliers of oil from the Middle East, they don't know . . .that a sacrifice zone the size of Florida has been created for the United States so the United States will have some degree of oil security for the next ten years. 1 2 Tar Sands Basics, 2012 OIL SHALE & TAR SANDS PROGRAMMATIC EIS, http://ostseis.anl.gov/guide/tarsands/index.cfm (last visited Apr. 28, 2012) [hereinafter About Tar Sands].3 Alberta Oil Sands, ALTA.GEOLOGICAL SURVEY, http://www.ags.gov.ab.ca/energy/oilsands/alberta_oil_sands.html
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.013 |
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".