Improving in-transit and in-theatre asset visibility of the Canadian Armed Forces supply chain network
Bibliographic record
Abstract
A key functionality in military logistics is the concept of sense and respond logistics to provide asset visibility, rapid response, and advanced agility tailored to all military decision levels: strategic, operational, and tactical. Future military forces do require a near-real-time in-transit and total visibility of assets, personnel, and vital supplies. The implementation of an end-to-end asset visibility (AV) model enabled by novel tracking and monitoring technologies, is essential toward reaching the desired goal. This paper proposes an integrated model for the Canadian Armed Forces (CAF) supply network suitable for asset visibility tracking and monitoring. It presents a high level design framework for end-to-end visibility and up to date asset information. It also discusses novel emergent technology, such as Next-Generation Wireless Communication (NGWC) mesh networking protocol (combined to active RFID) and RuBee, and its emerging challenges to successfully track CAF assets and provide in-theatre and in-transit visibility. NGWC technology provides an ultra-low power wireless protocol to collect and route logistics data to information systems which could be very effective in closing in-theatre and in-transit asset visibility gaps.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".