Bibliographic record
Abstract
US Silica running out, says CEO; Growth in Fairmount Santrol coarser sand; Northern White demand up says Smart Sand Frac sand producers reported surging sales and prices in the latest round of company results, driven by increased activity as well as a trend toward higher proppant densities. Over the first three months of 2017, US Silica reported sales of frac sand at 2.5m short tons, up 79% year-on-year, and an increase of 22% from the last quarter of 2016. Revenues from the company's proppant segment rose by 161% year-on-year, to $193m. The company's chief executive, Bryan Shinn, told investors that markets are expected to stay very tight. Most of the major sand suppliers, including US Silica, are running flat out today, Mr Shinn said. Demand is expected to continue growing faster than supply for the foreseeable future, and as such, we expect to continue pricing recovery and improving margins in our sand sales. Across the whole company, revenues rose 100% year-on-year, to $244.8m. The company reported a net profit of $2.5m in the first quarter, compared to a loss of $11.0m over the same period a year ago. Fairmount Santrol meanwhile reported raw frac sand sales volumes in the first quarter of 2017 at 1.9m s.tons. This is a rise of 36% year-on-year, and a 10% increase on the previous quarter.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.208 | 0.052 |
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".