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Record W622496336

Air Cargo Flattens Out

2009· article· en· W622496336 on OpenAlexaboutno aff
Aaron Karp

Bibliographic record

VenueAir transport world · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAir cargoForecast periodAir travelAviationOffset (computer science)Air transportQuarter (Canadian coin)BusinessEconomicsFinanceAeronauticsEngineeringTransport engineeringGeographyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This analysis of the air cargo market notes that air cargo volume soars when economies are healthy because the high cost of shipping by air is considered part of the cost of doing business when times are good, but that when economies contract, air cargo is among the first to be trimmed to lower costs. The near-future projections show low or flat growth in air cargo, with a recovery in late 2009, but only because late 2008 figures were so low. A major forecaster, Boeing Commercial Airplanes’ biannual “World Air Cargo Forecast,” calls it a “dire time,” and predicts that 2009 will be very tough, with some 18-24 months passing before a recovery. The September 2008 drop was the worst year-over-year decline since the dot.com collapse in 2001. With exports dropping, air cargo is also falling, as Chinese exports and U.S. imports have been the main drivers in recent years. Express operators are also expected to suffer declines. FedEx reported a 22 percent drop in net income for its most recent fiscal quarter. Longer-term, the Boeing forecast predicts air cargo traffic will grow 5.8 percent annually over the next 20 years. By 2027, it is expected to have tripled. It remains unclear whether this period of decline will be offset by above-average growth in demand.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.225
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

Machine predicted; both teacher heads agree on what is shown here.

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

Citations1
Published2009
Admission routes1
Has abstractyes

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