Human Factors Issues in Land Forces Weapon Systems Evaluations
Bibliographic record
Abstract
Human factors issues include the problems of operator performance, reliability, maintainability, availability, safety, and habitability as they relate to the interactions between the human, the machine, and the environment. Within the Canadian Forces (CF) many human factors activities are performed during the field evaluations that are conducted in support of the acquisition of weapon systems off the shelf'. This report is based on a review conducted in the early l99Os of some of the lessons learned' with regards to human- factors evaluations of off the shelf' and prototype systems conducted for the CF. From these field evaluations it is concluded that there is a need for increased emphasis on human factors at the requirements and concept development stages of system acquisition. Due to heightened interest in Human Systems Integration issues in procurement, the original review has been revised. Conclusions are drawn and recommendations made with respect to: 1) the need to plan for human factors evaluations; 2) common design deficiencies; 3) the limitations of human factors engineering techniques and need for further research, and; 4) the need to address human factors issues in Statements of Requirements (SORs) for new systems and equipment.
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 | 0.213 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".