Multiframe assignment tracker for MSTWG data
Bibliographic record
Abstract
In this paper, a multiframe assignment tracker is applied to the simulated data sets provided by the Multistatic Tracking Working Group (MSTWG). The multiframe assignment tracker solves the data association problem as a constrained optimization for fusing multiple sets of data to the tracks with an Interacting Multiple Model (IMM) estimator. The challenges with these data sets are high false alarm rate, low probability of detection and multiple synchronous/asynchronous sensors. Multiframe data association is used to perform data association, which is the crucial part of the tracking. Centralized tracking is used to optimally fuse the information from multiple sensors. A track's status is updated using an m out of n logic rather than the track quality based logic that requires more accurate probability of detection values, which are not available and vary with time and geometry in the MSTWG data sets. The results obtained with the multiframe assignment tracker for all the data sets are given in the form of MSTWG performance metrics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".