Bibliographic record
Abstract
Her Majesty the Queen as represented by the Minister of National Defence, 2001 Sa majeste la reine, reprcsentcc par la ministre dc la Defense nationale, 2001 Defence Research and Development Canada (DRDQ has been developing a low cost means to evaluate the integration of new display equipment for the Air Force. The result has been the Aircraft Crewstation Demonstrator (ACD), which is capable of simulating the cockpit of an aircraft for human factors evaluation (HFE). Li order to support fiiture display upgrades within the cockpit of the CF-18, DRDC contracted an HFE study of the CF-18 displays, especially the radar displays associated with radar and data link. Earlier work at the Defence Research Establishment Ottawa (DREO) had resulted in a high fidelity simulation of the air to air modes of a fighter radar. In order to develop as representative display as possible, a task to integrate the ACD with the DREO radar simulation as a distributed simulation was included with the HFE study. This report describes the use of the high level architecture (HLA) to combine these two disparate simulations into one distributed simulation. The results indicate that HLA is an effective means of combining different models to provide an improved simulation to the user.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.815 | 0.715 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".