Aviation Resource Management: Volume 2 - Proceedings of the Fourth Australian Aviation Psychology Symposium
Bibliographic record
Abstract
Contents: Selection: Job requirements of airline pilots: results of a job analysis, Peter Maschke, Klaus-Martin Goeters and Andrea Klamm Pilot selection: getting more bang for the buck, Eugene Burke, Alan Kitching and Colin Valsler The DMT down under: an Australian validation of the defence mechanism test, Andrew R. Lowe A personality test for aircrew selection: goats or sheep?, Martyn Roast, Helen Muir and John Harris Pilot selection procedures: a case for individual differences in applicant groups, Melissa M. Monfries and Phillip J. Moore Alternative approaches to gathering information in air traffic control selection research, Greg Hannan Selecting and training air traffic controllers ab initio: validation of a 1990s selection-testing program, Richard E. Hicks and Brian Keech. Training: The foundations of crew resource management should be laid during ab initio flight training, Steven J. Thatcher A new way to deliver an old message, Barrie Hocking Evaluating student pilotsa (TM) proficiency, Thomas Bluhm Learning by example: results from a global Internet study, David O'a (TM)are and Richard Batt Motivation and expectations in pilots and instructors regarding recurrent pilot training, Jens Rolfsen and Grete Myhre Structural knowledge concepts in airline pilots, Simon Henderson and Joey M. Anca GPS training for general aviation VFR pilots: to regulate or educate?, Ross St. George and Michael Nendick Future airline training: what has been learned from pilots and instructors?, Henry R. Lehrer, Phillip J. Moore, Ross A. Telfer and Aimee Freeman Stress in training transfer: cognitive interference, Heather J. Irvine and H. Peter Pfister The impact of executive control on trainee commercial pilotsa (TM) strategic flexibility, Susan L. Cockle and Phillip J. Moore Does facilitated group work and independent study in undergraduate pilot education improve learning and foster team skills?, Steven J. Thatcher Atmospheric science, air safety and essential weather briefing in student pilot training, Skye Hunter, Martin Babakhan and H. Peter Pfister. Human Factors: Benefits and future applications of 3D primary flight displays, Eddie L. Flohr Human factors issues in perspective display design, Neelam Naikar Evaluation of workload during a diversion using GPS and VOR, Benjamin Jobson and Michael Nendick The investigation of cognition in NVG helicopter operations, Peter F. Renshaw Musculoskeletal pain in S-70A-9 aircrew: a survey approach, David A. Foran and Anna M. Zalevski Active noise reduction in a helicopter environment, Anthony J. Saliba and Robert B. King. Air Traffic Control: Just another typical pilot error, Bert Ruitenberg Transition to the future: displaying flight progress data in air traffic control, Carol A. Manning Development of team resource management in European air traffic control, Manfred Barberino and Anne Isaac Air traffic control resource management for new automation: workload and workgroups, Lisa Duff, Michael Nendick and H. Peter Pfister Air traffic control in a screen-based non-radar environment: a preliminary evaluation of human factors in TAAATS, Greg Hannan, Phillip J. Moore, Claire Marrison, Geoffrey C. Ross and Ross A. Telfer A new approach to mental workload measurement in air traffic control, Charmaine E.J. HArtel, Andrew F. Neal, Graeme S. Halford and GA nter F. HArtel Developing measures of situation awareness, task performance and contextual performance in ATC, Andrew F. Neal, Mark A. Griffin, Jan Paterson and Prashant Bordia The human-machine interface in air traffic control: task analysis of existing ATC, Hiroki Sato and David Rackham Making the link between human factors and organizational learning, Christine Owen. Maintenance: Maintenance engineering training needs of the Pacific Islands commercial aviation industry, Michael J. Terim and H. Peter Pfister Maintenance human factors: learning from errors to improve systems, Alan Hobbs. Situational Awareness: Situational awareness or metacognition?, Graham Beaumont Individual differences in situational awareness and training for complex tasks, David O'Hare and Kerry O' Brien Decision-making under time constraints, Mark Wiggins and P. Anderson. Developmental Workshops Reports: Air traffic control developmental workshop report, Bert Ruitenberg, Anne Isaac, Carol Manning and John Guselli Aircraft maintenance developmental workshop report, Nick McDonald, Alan Hobbs and Michelle Robertson Situation awareness developmental workshop report, Simon Henderson, Mica Endsley and Brent Hayward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.019 |
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".