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Railway transportation of dangerous goods: a bibliometric aspect

2019· article· en· W2980868182 on OpenAlexfundno aff
T. O. Kolesnykova, Olena V. MATVEYEVA, Lev Manashkin, Maxym Mìshchenko

Bibliographic record

VenueMATEC Web of Conferences · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEconomic and Technological Systems Analysis
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesSocial Sciences and Humanities Research Council of CanadaNational Research Council CanadaInstitut d'Estudis CatalansAlberta InnovatesCentre National pour la Recherche Scientifique et TechniqueMitacsBulgarian National Science FundDwight D. Eisenhower Army Medical CenterCity, University of LondonNorthwestern UniversityMinistry of Infrastructure, Transport and NetworksGeneralitat ValencianaNatural Sciences and Engineering Research Council of CanadaRutgers, The State University of New JerseyOffice of the Assistant Secretary for Research and TechnologyNational University Rail CenterNational Natural Science Foundation of ChinaNorthwestern University Transportation CenterU.S. Department of Transportation
KeywordsTelematicsScopusTransport engineeringCitationDangerous goodsData scienceField (mathematics)Empirical researchThematic mapComputer scienceBusinessEngineeringWorld Wide WebPolitical scienceGeographyTelecommunications

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1660.282
Science and technology studies0.0010.001
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.021
GPT teacher head0.214
Teacher spread0.193 · 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

Labeled directly by 2 models reading the full record.

Study designObservational
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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

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