National Aerospace Planning Process Enhancements: Analysis and Innovation
Bibliographic record
Abstract
Abstract : New advanced decision support technology concepts have been developed to support Air Domain Awareness (ADA) and the National Aerospace Planning Process (NAPP). This report reviews and validates the NAPP requirements based on consultations with 1 Canadian Air Division, assesses relevant existing tools and technologies, tabulates promising research directions, and proposes a set of innovative improvements for implementation in a NAPP Enhancement Prototype NEP. Four ADA innovations, hosted in Google Earth, are proposed. These will enable NEP to better support visualization of sensor coverage, detect coverage gaps, visualize future weather, and analyse dynamic threats to vital points. New visual analytics tools for NEP are proposed that will reveal subtle long-term temporal, geospatial, and behaviouralpatterns for Resource Awareness and Total Air Resource Management (TARM).NEP will include novel tools for air force resource visibility and resource management, including tools for vertical awareness (down to the Wings and squadrons), and horizontal awareness (forward and backward in time). Asset availability awareness is described based on Dashboard and Magnets Grid visualizations. A Hockey Card metaphor encapsulates the key elements of each mission. To rapidly respond to un-forecast events, a resource management app scans existing Air Tasking Orders and proposes viable re-planning solutions based on: rapidity of response, ability to dwell if required, and the availability of an appropriate payload.This is the first of two reports. The second report documents the subsequent design and implementation of the NEP, and its demonstration to the Air Force.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".