TREND: Bureau of Transportation Statistics. Airline Performance - Departures: Airline Departing Flights | State: California | Airport Code: ONT | Airport Name: Ontario, CA: Ontario International, 1987 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-002-002
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@cbe Dataset: Number of departing flights. Note, this number is identical to arriving flights at the national (all) and airline levels, but different on all other levels. 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. http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time Category: Industry, Business, and Commerce, Transportation and Traffic Subject: Air Transportation, Airline Operations, Passenger AirTransportation, Airlines, Flight Departures, Business Travel, Airports, Airline Industry, LeisureTravel 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. https://www.bts.gov/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.110 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".