A modified Murty algorithm for multiple hypothesis tracking
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Bibliographic record
Abstract
In this paper, we present two practical modifications of the original Murty algorithm. First, the algorithm is modified to handle rectangular association matrix. The original Murty algorithm was developed for a square matrix. It is found that the expanding rules should be changed so that the cross-over pair within an assignment can be extended to the last column and can be repeated for the last column upon certain conditions. The second modification is the allowance of an "infeasible" assignment, where some tracks are not assigned with any measurements, therefore, good "infeasible" hypotheses are maintained and clutter seduced hypotheses are suppressed when the information evidence becomes stronger. Examples are used to demonstrate the modifications of the existing Murty algorithm for a practical implementation of an N-best Multiple Hypothesis Tracker.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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