Bibliographic record
Abstract
Prior to joining Temasek Polytechnic, Gary started his career as an Air Traffic Control Officer and later, as a Terminal Manager, in the Civil Aviation Authority of Singapore (CAAS). After completing his MBA and stint in Montreal, where he helped out at the Singapore ICAO office and coordinated the implementation of Common Use Self Service Kiosks at Montreal Trudeau International Airport, Gary returned as Manager, Airport Management and Air Cargo, at the investment arm of the CAAS, Changi Airport Managers and Partners (CHAMPS) where he was actively involved in consulting work for potential investments in, travelled to and lived in New Delhi – India, Nanjing – China, Kunming – China, Tokyo – Japan and Mexico City – Mexico. In 2006, Gary joined ARINC Inc. as Account Manager for Asia Pacific as part of the pioneer team. He converted and managed ARINC sites in Asia Pacific including Singapore Changi, Cambodia - all 3 international airports and New Delhi, India. Gary holds an International Aviation MBA from Concordia University (2003), a Degree in Business (Marketing) from the Nanyang Technological University of Singapore (1999), and is currently pursuing his Ph.D. in Aviation at Embry Riddle Aeronautical University. He is concurrently an Instructor for the Institution of Engineers Singapore Professional Certificate in Project Management and also provides aviation consultancy through Temasek Polytechnic.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.727 | 0.448 |
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".