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Record W2802355496 · doi:10.38008/jats.v2i2.99

AIR FREIGHT AND MERCHANDISE TRADE: TOWARDS A DISAGGREGATED ANALYSIS

2012· article· en· W2802355496 on OpenAlexaff
Franziska Kupfer, Hilde Meersman, Evy Onghena, Eddy Van de Voorde

Bibliographic record

VenueJournal of Air Transport Studies · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsTransport Canada
Fundersnot available
KeywordsAir cargoBusinessProfit (economics)Air transportIndustrial organizationAir travelProduct (mathematics)International tradeCommerceEconomicsAviationTransport engineeringEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

During the past 30 years, air cargo has evolved from a by-product to a potential profit centre for airlines. However, the success in the air cargo business depends on a number of factors. The evolution of world merchandise trade and particularly, the trade in high-value goods, is one of the determinants of the demand for air freight services. This paper provides an insight into the relationship between air cargo and merchandise trade on an aggregated as well as on a disaggregated level. Special attention is paid to the air cargo flows between major regions. By combining several levels of the air cargo market, this paper explains part of the economic rationality behind the air cargo market structure. The results of this paper will lead to a better knowledge of the air cargo sector, not only by academics but also by industry actors.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.064
GPT teacher head0.274
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2012
Admission routes1
Has abstractyes

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