<title>ID box: a multisource attribute data fusion function for target identification</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The R&D group at Lockheed-Martin Canada has developed a target identifier function called ID Box. This computer program performs five main functions: first it transforms the sensor attribute input into a few contact ID declarations, second, it evaluates the association score between the contact declarations and the ID propositions of a current target track, third it performs attribute contact to track fusion using a modification of the Dempster-Shafer evidential theory, fourth the ID Box, using a platform library, produces a translator that unifies the information within track identity and the attribute input, and fifth, it manages the distribution of results to a system human computer interface. Our exhaustive platform library enables the ID Box to fuse attribute data from almost all kinds of sensor or information sources that may be found on large warships or patrol aircraft. These attributes are the radar cross section and the moving parts from surveillance radars, allegiance from interrogator systems, emitter composition from electronics support measure systems, spoken language from communication intercept systems, acoustical signature from sonar systems, propulsion types from IR detectors, dimensional data from imaging systems and other classification attributes from various systems or operators including dynamical parameters from positional trackers. This paper presents and describes the ID Box.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it