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Record W560685502

LONGER FREIGHT TRAINS: POSSIBILITIES AND LIMITATIONS

2000· article· en· W560685502 on OpenAlexaboutno aff
H. Vogel

Bibliographic record

VenueRail international · 2000
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsTrainFreight trainsTransport engineeringRail freight transportEngineeringBogieAutomotive engineeringOverhead (engineering)TelecommunicationsElectrical engineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.192
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2000
Admission routes1
Has abstractyes

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