Bibliographic record
Abstract
Rig counts for onshore drilling, a major end market for silica (frac) sand, barite (barytes), bentonite and proppant minerals, continued to drop globally, particularly in North America, in the first half of the year, although by the third quarter the pace of decline had slowed. Changes in technology used by oil and gas exploration firms, as companies drill more stages per well in order to cut costs, have led to the price of oil becoming more detached from rig count figures and a less concrete indicator of demand for oilfield minerals, as more frac sand is being used per well. In Q2, US-based Eagle Materials Inc. reported a 68% decline in frac sand volumes and financial losses were reported by other oilfield mineral players including Baker Hughes Inc., Halliburton, Carbo Ceramics Inc. and Fairmount Santrol Inc. Logistics costs have become increasingly relevant to frac sand profit margins, prompting some suppliers to integrate distribution capacity. Trump has expressed his avid support for the fossil fuels sector and put forward plans to open onshore and offshore rig leasing on federal land, eliminate moratoria on coal leasing and open shale energy deposits, with the aim of becoming independent of imported energy from the...
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 | 0.001 | 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) | 0.000 | 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".