State of the art in RTM technology for phase 2 trials
Bibliographic record
Abstract
This report provides a survey of a number of key enabling technologies for implementation of RTM Services. It continues on an earlier work and report by the authors in 2020, which covered the following capabilities: 1) E-Identification and Situational Awareness, and 2) Flight Planning and Strategic Flight De-confliction. In that report, first performance requirements of these services were discussed in details and examples from literature and other jurisdictions were provided. The second part of that document presented several examples of commercial technologies related to these services. The present report continues on the earlier work and explains the state of the art of the key enabling technologies for RTM essential services, including: 1) Geo-fencing 2) Collision Avoidance and Detect and Avoid 3) Surveillance and Tracking 4) Communications and Cybersecurity 5) Navigation While the RTM architecture and services have been defined in recent Transport Canada documents, there is little published work related to the problem of characterization of the performance requirements and effectiveness of the services themselves. In this report, the authors first adapt a model initially introduced by NASA and propose a framework and description for the performances of the RTM services. Then, the performance requirements and proposed measures of performance, specifically for each services are described in Table 3.
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 | 0.065 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".