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Record W4312533463 · doi:10.26163/raen.2019.69.57.002

Green Technologies in Transport Logistics: Russian Business Experience

2019· article· ru· W4312533463 on OpenAlex

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

VenueВЕСТНИК ОБРАЗОВАНИЯ И РАЗВИТИЯ НАУКИ РОССИЙСКОЙ АКАДЕМИИ ЕСТЕСТВЕННЫХ НАУК · 2019
Typearticle
Languageru
FieldBusiness, Management and Accounting
TopicEconomic and Technological Systems Analysis
Canadian institutionsTransport Canada
Fundersnot available
KeywordsGreen logisticsBusinessBusiness logisticsIndustrial organizationProcess managementEnvironmental economicsMarketingEconomicsSupply chain managementSupply chain

Abstract

fetched live from OpenAlex

Статья посвящена анализу опыта российских компаний в сфере применения «зеленых» логистических технологий. Рассмотрены примеры «экологизации» логистики при перевозках различными видами транспорта. Сделаны выводы о перспективах использования «зеленых» технологий в транспортной логистике в России. The research is devoted to analyzing Russian business experience in applying green logistic technology. We consider the examples of ecologization of logistics when using different kinds of transport. We make conclusions about the prospects of green technologies in transport logistics in Russia.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0040.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0100.011

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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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