Bibliographic record
Abstract
Abstract : The Canadian Forces (CF) trains in a wide variety of skills. For reasons of efficiency and economy, it is important to conduct no more refresher training than is necessary to keep performance at the desired skill level. This work surveyed the scientific and technical literature for models of skill fading and tools for determining when refresher training is required. In particular, the literature review investigated models for predicting skill retention relevant to the military domain. Subjective approaches, qualitative approaches, and quantitative have been used to model skill retention. Currently the U.S. Army Research Institute's Users' Decision Aid (UDA) model is the most advanced model. The UDA is one of few approaches for which empirical research has been done to assess its applicability and practicality. The UDA has been demonstrated to be relatively easy to administer, although some training is needed to perform the ratings correctly and reliably. The UDA itself is low-cost and requires no special equipment. The UDA, however, has received empirical validation from just one study that reports a comparison between UDA predictions and actual retention data. The literature review identifies a set of lessons learned and discusses five specific recommendations.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".