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Record W3184918235 · doi:10.1007/s40534-021-00242-1

The trajectory of hybrid and hydrogen technologies in North American heavy haul operations

2021· article· en· W3184918235 on OpenAlex
Kevin Oldknow, Kyle Mulligan, Gordon McTaggart-Cowan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRailway Engineering Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsCanadian Sport Centre PacificSimon Fraser University
Fundersnot available
KeywordsSnapshot (computer storage)Context (archaeology)Emerging technologiesPropulsionHydrogen technologiesTransport engineeringComputer scienceEngineeringSystems engineeringHydrogen economyFuel cellsHydrogen fuelAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The central aim of this paper is to provide an up-to-date snapshot of hybrid and hydrogen technology-related developments and activities in the North American heavy haul railway setting, placed in the context of the transportation industry more broadly. An overview of relevant alternative propulsion technologies is provided, including a discussion of applicability to the transportation sector in general and heavy haul freight rail specifically. This is followed by a discussion of current developments and research in alternative and blended fuels, discussed again in both general and specific settings. Key factors and technical considerations for heavy haul applications are reviewed, followed by a discussion of non-technical and human factors that motivate a move toward clean energy in North American Heavy Haul systems. Finally, current project activities are described to provide a clear understanding of both the status and trajectory of hybrid and hydrogen technologies in the established context.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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