Bibliographic record
Abstract
Although almost unknown in Europe, ore railways, carrying primarily mineral freight, especially iron ore or coal, are important in the USA, Canada, and Australia especially. They have exceptionally high performance standards, because they use state-of-the-art technology in their field. Their productivity per employee, wagon, or locomotive is about 20 to 40 times the European average. A typical ore railway is single track, running several hundred km from mine to port, and designed for bulk transport, with annual traffic 20Mt to 100Mt. Ore railways have three major advantages over other railways. They have no rivals on the routes over which they operate. They are safe investments because of their potential for continual development. They have unique heavy haul specialisation, so that their performance is very good, except for speed, which is irrelevant. The International Heavy Haul Association (IHHA) has as members ore railways and some other larger railways also operating mining lines. Most investors are interested only in new ore freight lines, which have been under construction or planned in Russia, India, China, Indonesia, Australia, North Africa, South Africa, Brazil, Mexico, Nicaragua, and Panama. This article includes a discussion of examples from Brazil, Australia, Canada, and Africa.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".