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Record W4243646467 · doi:10.1108/oxan-db212746

Pressures on US airlines may prompt protection efforts

2016· other· en· W4243646467 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2016
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsProfitability indexQuarter (Canadian coin)BusinessBrexitLow-cost carrierFinanceInternational tradeMarketingEuropean union

Abstract

fetched live from OpenAlex

Subject US airlines' Q2 2016 earnings. Significance Following several years of profitability after decades of losses, the US airline industry could be returning to old ways, with lower-than-expected second-quarter earnings for this year. Although the major carriers are showing some discipline in not increasing capacity to maintain market share, financial yields per passenger are down, an indication of emerging problems for the sector. Impacts Early hedging of fuel prices is likely to stave off shorter-term pressures on airlines should oil prices rise. The retirement of an older generation of pilots will enable carriers to reduce labour costs with new hires. Brexit will pose a number of regulatory uncertainties for US airlines' transatlantic flights to the United Kingdom.

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)0.003
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.142
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.1420.030

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 head0.037
GPT teacher head0.255
Teacher spread0.219 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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