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Record W6920322312 · doi:10.6068/dp14baee5ade394

Trend 1987 - 2014. Bureau of Transportation Statistics. Departures (Airline Performance): Departures - Security Delay | Country: USA | State: Massachusetts, 1987-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 007-002-012.

2015· other· en· W6920322312 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAviationNonStopQuarter (Canadian coin)CONQUESTGround transportationTransit (satellite)Air traffic controlPoint (geometry)

Abstract

fetched live from OpenAlex

Bureau of Transportation Statistics (2015). Departures (Airline Performance): Departures - Security Delay | Country: USA | State: Massachusetts, 1987-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 007-002-012. Dataset: Security: Delays or cancellations caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas. Airline on-time data are reported each month to the United States Department of Transportation (DOT), Bureau of Transportation Statistics (BTS) by US air carriers that have at least 1 percent of total domestic scheduled-service passenger revenues, plus additional carriers that report voluntarily. The data cover nonstop scheduled-service flights between points within the US, as described in 14 CFR Part 234 of DOT regulations. This dataset contains on-time departure performance data. Gate departure time is the instance when the pilot releases the aircraft parking brake after passengers have loaded and aircraft doors have been closed. Data are reported for the nation, by state, and by airport and airline. Airline mergers result in various reporting changes over the period. Joint reporting begins after Federal Aviation Administration approval of a single operating certificate. (1) January 2006: US Airways (US) and America West (HP) start to report jointly as US Airways (US). (2) January 2010: Delta (DL) and Northwest (NW) start to report jointly as Delta (DL). (3) January 2012: United (UA) and Continental (CO) start to report jointly as United (UA). (4) January 2012: Atlantic Southeast (EV) and ExpressJet (XE) start to report jointly as ExpressJet (EV), presented here as ExpressJet Combined (E*). For data through December 31, 2011, use the code EV for Atlantic Southeast and the code XE for ExpressJet. Beginning with January 1, 2012 data, use the code E* for ExpressJet Combined. Category: Transportation and Traffic, Industry, Business, and Commerce Source: Bureau of Transportation Statistics The Bureau of Transportation Statistics (BTS) was established as a statistical agency in 1992. The Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 created BTS to administer data collection, analysis, and reporting and to ensure the most cost-effective use of transportation-monitoring resources. BTS brings a greater degree of coordination, comparability, and quality standards to transportation data, and facilitates in the closing of important data gaps. http://www.bts.gov/ Subject: Air Transportation, Airline Industry, Leisure Travel, Flight Departures, Airlines, Airline Operations, Airports

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0050.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.012

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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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