Accurate Murty's algorithm for multitarget top hypothesis extraction
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Bibliographic record
Abstract
In most hypothesis-oriented Multiple Hypothesis Tracking (MHT) implementations, the target-to-measurement data association is typically solved by using the Murty's algorithm. However, the Murty's algorithm has no control over the diversity of target-to-measurement associations — often the top associations vary only slightly. In addition, in practical tracking solutions, tracks are often grouped as tentative or continued. It was observed with real data sets that in the associations, the top hypotheses consist of mostly similar associations with the same confirmed tracks along with some permutations of new measurements. The result is that a fixed set of confirmed tracks dominate diversity of the association tree. To overcome this problem, a modified Murty's algorithm, which can achieve any user defined (or adaptable) diversity of track-to-measurement association of different types of tracks, is proposed in this paper. Numerical examples are provided to demonstrate the improved efficiency in hypotheses generation by the proposed method.
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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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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