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Record W2139254431 · doi:10.1504/wremsd.2013.054736

Regional jet aircraft competitiveness: challenges and opportunities

2013· article· en· W2139254431 on OpenAlex
Tamilla Curtis, Dawna L. Rhoades, Blaise P. Waguespack suffix Jr. suffix

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Review of Entrepreneurship Management and Sustainable Development · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueAviationJet (fluid)Range (aeronautics)LagBusinessAircraft industryAeronauticsFinanceEngineeringAerospace engineeringComputer science

Abstract

fetched live from OpenAlex

The regional jet aircraft is a unique market niche. Particularly suitable for providing capacity in the 30 to 90 seat range, these jets are often used to connect smaller airports to network carrier hubs, as well as to fill in during slow periods. The market is currently dominated by two manufacturers: Brazil’s Embraer and Canada’s Bombardier. Due to the nature of the global aircraft industry, Embraer and Bombardier are largely dependent on the international sale of their aircraft for steady revenue streams. Orders and deliveries of aircraft with fewer than 100 seats have grown rapidly over the past ten years. The study provides an overview of the aviation industry, particularly in the regional jet (RJ) sector, and examines country-specific factors affecting the number CRJ and ERJ deliveries. Results of stepwise regression indicate that a two-year lag of GDP, a two-year lag price of crude oil, a two-year lag of prior aircraft deliveries, and the country-specific land areas account for almost 40% of the variance in the aircraft deliveries. However, there are many additional factors which have an effect on RJs orders and deliveries.

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.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.064
GPT teacher head0.231
Teacher spread0.167 · 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