Joint class identification and target classification using multiple HMMs
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
Target classification has received significant attention in the tracking literature. Algorithms for joint tracking and classification that are capable of improving tracking performance by exploiting the interdependency between target class and target kinematic behavior have already been proposed. In these works, target identification relies on the a priori information about target classes, but, in practice, the prior class information may not always be available or not accurate. This motivates the design of a new estimation method that can jointly build target classes and classify targets even when a priori information is not available. Based on the generic expectation-maximization framework, a novel joint multitarget class estimation and target identification algorithm that requires only target feature measurements is proposed in this paper to achieve this goal. In this approach, multitarget classes are characterized by multiple hidden Markov models. Besides theoretical derivations, simulations are presented to verify the effectiveness of the proposed algorithm.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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