<title>ID box: a multisource attribute data fusion function for target identification</title>
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.036 |
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".