CO<sub>2</sub> Narcosis as a Root Cause of Unexplained Physiological Events in High Performance Aircraft
Bibliographic record
Abstract
Since 1991, there has been an alarming increase in the number of unexplained physiological events (UPEs) reported and experienced by pilots of jet fighters across different fleets. The UPEs have resulted in grounding some airframes, loss of aircraft, and even loss of life. There is no single agreed-upon root cause of UPEs that has been identified, and therefore there is no reliable corrective action. This author analyzed the literature related to other industries where artificial hyperoxic gas mixes are employed and where similar adverse reports have been reported. I hypothesize that UPEs are caused by high-dose oxygen delivery in excess of officially approved oxygen schedules while airflow rates are often inadequate, at a time when the positive pressure breathing feature of their oxygen regulator is not used. In a setting where pulmonary vital capacity is adversely affected by G-maneuvers and oxygen- and G-induced atelectasis, tidal volume is reduced by flight gear, and effective gas exchange is not supported by adequate ventilation, these factors combine to produce respiratory acidosis, followed by acute respiratory distress syndrome, CO2 narcosis, and coma. Reports from field data related to incidents in F-18S/H, showing that emergency oxygen did not correct the hypoxia-like symptoms including long-lasting periods of incapacitation and prolonged headaches, lend support to our hypothesis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".