A Mentored Experience Accumulation Differential Model: Rapid Parameter Space Analysis Applied to Royal Canadian Air Force Pilot Production, Absorption and Retention
Bibliographic record
Abstract
Since the early 2000s, the Royal Canadian Air Force (RCAF) has used a detailed personnel training model-Pilot Production, Absorption, Retention Simulation (PARSim)-to study the progress of pilots from recruitment to release. The model captures key dynamics of pilot career throughput, with particular attention paid to the upgrade of inexperienced pilots arriving at operational squadrons via mentoring by experienced pilots. Here we develop a simplified model of the same career structure, based on systems of differential equations, that captures the fundamental dynamics and constraints of the full PARSim model but enables rapid analysis of the parameter space via numerical simulation to produce a higher level view of pilot occupation health. A further advantage of this model is that, within certain domains, the equations can be solved analytically which provides valuable insights into the system's stability, steady state, and critical conditions in terms of the model's fundamental parameters.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".