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Record W659534410

IT GOT WORSE : US MAJOR AIRLINES FOLLOW UP A YEAR OF RECORD LOSSES... WITH ANOTHER YEAR OF RECORD LOSSES

2003· article· en· W659534410 on OpenAlexaboutno aff
Paul L. Flint

Bibliographic record

VenueAir transport world · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueQuarter (Canadian coin)BailoutGovernment (linguistics)BusinessFinanceEconomicsAgricultural economicsGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

A year of record losses for the U.S. majors has been followed by another year of record losses. The 10 largest U.S. airlines collectively lost more money in 2002 than in 2001. That figure includes the $5 billion government bailout distributed in 2001. Again, Southwest did the well, emerging as the only profitable carrier in the group for both periods. The biggest loser was American Airlines parent, AMR Corp. Holiday traffic was stronger than anticipated, creating a fourth-quarter bump, but other unusual charges took away from the bottom line. Fourth-quarter operating revenues rose 14.2%, reducing the combined operating loss to $3.4 billion from $4.8 billion. Revenues for the year declined 7.3%, while operating costs dropped 6.7%. Operating losses for AMR were $3.33 billion, versus $2.47 billion. UAL ended about $300 million than AMR. Figures for other majors are included, along with charts of yields and costs.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.219
Teacher spread0.191 · 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 teacher head, not a consensus.

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

Citations0
Published2003
Admission routes1
Has abstractyes

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