Bibliographic record
Abstract
In Alberta, Canada, oil companies are now exploiting tar sands. Strip mining has left the ground skinned and gutted and an increasingly common method of extracting the oil is even more damaging to the environment. The Canadian tar sands contain an estimated 170 million barrels of recoverable oil. Nowadays most operations dig up the tarry bitumen in open pit mines, then separate it from the sand and refine it. The high energy process damages the environment, leaving lakes of toxic residues, and a high carbon cost. Researchers hope new technologies can be used in a process which will help to reduce greenhouse gases and help to transform the bitumen into lighter oil underground, before it is pumped to the surface. Many of the toxic residues can remain underground, removing the problem of surface pollution. Another solution is to induce bacteria to digest the bitumen converting it into methane which could be extracted like conventional natural gas. This innovative solution is still at the field-testing stage; if it is successful researchers believe further work on the technique could eventually result in the ability to extract and refine a zero-carbon fuel.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".