This page intentionally left blank. DND/CF Network Enabled Operations
Bibliographic record
Abstract
Statement outline the range of security challenges that Canada faces. The Canadian Forces (CF), supported by the Department of National Defence (DND), are responsible for providing a rapid and effective response to these security challenges as directed by government. To carry out the wide range of operations expected of them, the CF must be flexible, combat-capable and able to work closely with domestic security partners and international allies. Network Enabled Operations 1 (NEOps) provides one of the main means of accomplishing this. 2.0Aim DND and the CF have been thinking about NEOps for a number of years. The aim of this working paper is to assist in the development of an integrated, coordinated way ahead on NEOps by setting out a common understanding of this concept, highlighting its scope, benefits and implications, and providing a notional roadmap to exploit its potential. The implementation of NEOps across DND and the CF will evolve further as the capabilities required to implement the Defence Policy Statement are further defined in the forthcoming Defence Capabilities Plan and other Departmental policy and planning documents. 3.0Overview
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.072 | 0.005 |
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; both teacher heads 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".