Author.. n ~....... ~. '..........................................--·-·------·----· ·..
Bibliographic record
Abstract
© Sa majeste la reine, representee par le ministre de la Defense nationale, 2001 DREA TM 2001-212 i Shipboard Command and Control (C2) presents unique challenges for decision support. Tactical decisions require that the ship’s Command Team gain timely access to and comprehend the significance of large amounts of information that may impact on the mission. While operations depend heavily on doctrine and standard procedures, many tactical details must be established in real time, particularly as unanticipated events or anomalous situations arise. This imposes significant cognitive demands on operators for active situation assessment and decision making, where benefits for performance improvements may be expected from incorporating advanced support tools like decision support systems and integrative work aids and displays. Cognitive Work Analysis (CWA) is a layered, systems-based analysis framework that specifically addresses system design to support operators in unanticipated situations. Its first layer is a work domain analysis (WDA), which develops hierarchical models representing the intentional, functional, and physical properties of the work domain, at different levels of abstraction, as well as the relations between these levels. In this document, we discuss the results of a year-long study of the application of WDA to tactical C2 for the Canadian Navy’s HALIFAX Class frigate. We review the WDA models that resulted and briefly describe some applications of the CWA approach in
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.818 | 0.795 |
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".