Bibliographic record
Abstract
Long heavy freight trains are in regular scheduled operation in Russia, the USA, Canada, Brazil, South Africa, and Australia. The heaviest trains so far have had a gross load of 40,000t, and trains with 24,000t loads are not uncommon. Many trains are over 3km long. These railways usually separate freight and passenger traffic as far as possible, and use only bogie wagons to carry freight. The advantages of longer heavier trains include: (1) greater throughput and thus greater capacity on congested lines; (2) no need to build double tracks or passing tracks; and (3) savings in numbers of locomotives and locomotive drivers. This paper presents an investigation by Swiss Railways (CFF/SBB) of the possibility of placing longer freight trains in service on the north-south main line via the Gotthard Tunnel under the Alps. The reasons for the study were related to threatened congestion on part of the rail network and the need to find more cost-effective freight transport. Aspects considered included journey times, traction, power supply and overhead lines, and signalling installations. The tests conducted showed that freight trains up to 1.5km long would be feasible if their individual wagons had relatively uniform braking properties; it would be necessary to use locomotives remotely controlled by radio.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".