Bibliographic record
Abstract
Despite aggressive efforts in spatial disorientation (SD) research, hardware development, and training, the operational impact of SD in terms of crew and aircraft losses remains significant. Current training in spatial orientation is primarily composed of didactic lectures on the anatomy and physiology of the sensory systems. Significant efforts have been concentrated on reproducing various types of visual and vestibular "illusions" that pilots might encounter in flight, with limited and varying success. Unfortunately, the terms of "SD" and "illusion" have been used synonymously, leading to the general belief that if one were to be exposed to a specific type of illusion, one can prevent or avoid SD mishaps. Another setback is the inability of ground-based devices to reproduce the flight envelope. Often the demonstration of a specific illusion ends abruptly without further explanation or how these illusions can affect pilot performance. Demonstration of illusions seldom deals with the precipitating factors. We should provide pilots with skills to anticipate and assess the risk of SD during mission planning. Pilots should be sensitized to the physical and mental performance decrement during sensory conflicts and inadequacies. Recommendations should also be made on possible ways to recover from SD should they become disoriented. Special attention should be drawn to the properties of various flight displays that may contribute to SD. G tolerance and disorientation should be examined together in high performance aircraft as there is a close relationship between exposure to acceleration and maintaining orientation. The motto for counteracting SD is: anticipate, avoid, and counteract SD.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".