SORTING OUT COAL : TO GROW COAL TRAFFIC, CLASS IS NEED TO SCORE IN SERVICE AND MARKETING
Bibliographic record
Abstract
With demand for coal subject to wide fluctuations based on weather, price of natural gas and emissions restrictions, railroads that rely on coal for a large part of their revenues are seeking ways to used improvements in service and efficiency to make the most of the coal traffic they do get and market it better within their networks. The U.S. is still a huge coal producer, second only to China, with reserves projected to last another 250 years at current levels. However, the power industry, which consumes roughly 90% of all coal produced is aggressively stepping up conversions to natural gas. Still coal tonnage is rising because utilities are upping capacity overall. Also, utilities have improved their inventory practices, and railroads have improved their service to the point that utilities only need 20- or 30-day supplies on hand versus 60-days. The article describes environmental laws and industry developments affecting coal shipping, including a renewed concentration on Canadian markets.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.015 |
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".