Predicting Success in the Tactical Air Combat Party Training Pipeline
Bibliographic record
Abstract
OBJECTIVES: To develop a statistical model that predicts the likelihood of success or failure of military training candidates using tests administered before initial skill training as inputs. METHODS: Data were acquired from candidates before the start of U.S. Air Force Tactical Air Control Party training, including (1) demographic, (2) psychological composition evaluated using Emotional Quotient Inventory, (3) physical performance capability, (4) a physical activity questionnaire, and (5) salivary fatigue biomarker index. A total of 126 candidates were tracked until they either passed or failed the training, and a total of 55 variables were used as inputs for creation of the model. RESULTS: Clustering analysis of the data revealed that only 4 of 55 variables were useful for predicting success or failure. The variables in the order of their importance are as follows: run time, number of miles run per week in the last year, level of salivary fatigue biomarker, and height. CONCLUSIONS: The results suggest that simple testing methods can identify candidates at high risk of failure.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".