Multi-Sensor Integration within the Common Operating Environment (MUSIC) Project Data Collection Requirements for the Atlantic Littoral ISR Experiment
Bibliographic record
Abstract
© Sa majesté la reine, représentée par le ministre de la Défense nationale, 2004 The Multi-Sensor Integration within a Common Operating Environment (MUSIC) Technology Demonstrator Project (TDP) has been established to demonstrate a capability for surveillance data fusion within the Navy’s Recognized Maritime Picture (RMP) and to identify a suitable, scalable computing architecture in which such fusion can take place. In the summer of 2004, the Canadian Forces Experimentation Centre (CFEC) is undertaking a large-scale experiment known as the Atlantic Littoral ISR Experiment (ALIX). ALIX represents a significant opportunity to the Defence R&D community in terms of the volume of available surveillance data collected and the associated challenge of generating a Recognized Maritime Picture (RMP) from this data. The MUSIC project needs to establish its data collection requirements in order to take full advantage of this event. This report provides an overview of the metric that the data collection must support and an overview of past trial experience in support of sensor integration research. These metrics and lessons learned are used as the basis for the data collection requirements of the MUSIC TDP during the ALIX
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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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".