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Record W3080706065 · doi:10.54648/aila2020069

International Air Cargo in Time of Crisis: Global Challenges and Modal Shift Provide Transformational Opportunity in Commerce and Law

2020· article· en· W3080706065 on OpenAlexaffabout
George Leloudas, Daniel B. Soffin

Bibliographic record

VenueAir and Space Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsMultimodal transportSupply chainAir cargoConventionPaceAviationBusinessIndustrial organizationInternational tradeEngineeringPolitical scienceLawTransport engineeringMarketingGeographyAerospace engineering

Abstract

fetched live from OpenAlex

The current pandemic has elevated the critical need for a dependable, resilient, air cargo supply chain to the forefront of commercial and legal discussion. Throughout its history, air cargo has evolved in parallel with other unimodal means of cargo transport into a global multimodal transport paradigm. The unimodal legal regimes governing individual modes of cargo transport have not kept pace with the growth and development of global cargo transport and its associated legal issues. The unimodal regimes do not adequately reflect the relational, commercial, and operational realities of the contemporary global multimodal supply chain. The authors identify key changes in air cargo and multimodal supply chain logistics and relationships, and discuss the resulting contemporary legal issues that have emerged and must be addressed. The authors advocate that a multimodal cargo transport regime is needed to reflect these current realities and to accommodate future multimodal evolution. At a minimum, update and integration of the Warsaw, Montreal, and CMR conventions is indicated. The authors emphasize the essential need for continued integration of new technologies and complete digitalization of the air cargo supply chain in order adequately to prepare for the global multimodal cargo transport paradigm of tomorrow. aviation, air cargo, CMR, Warsaw Convention, Montreal Convention, unimodal, multimodal, global multimodal supply chain, digitalization, technology

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0130.008
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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 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
Published2020
Admission routes2
Has abstractyes

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