Weight partitioned Probability Hypothesis Density filters
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
The Probability Hypothesis Density filter gives an estimate of the multistate solution set without a multidimensional assignment between measurements and the target estimates. The filter itself outputs a multimodal surface from which individual target estimates must be manually extracted. Furthermore, since the filter propagates the entire multistate estimate, it does not provide any natural connection between any individual state estimates extracted at consecutive timesteps. Recently a new series of deconvolution methods known as CLEAN algorithms have been explored in the particle-based PHD context as a new method of state extraction which considers both the weight and spatial properties of the state estimates. This paper explores weight based state extraction in PHD filters in a more general context and focuses on the issue of track continuity when using weight partitioned PHD filters. The partitions are maintained over time, based on their spatial and weight characteristics so to represent individual or singleton estimates at each timestep.
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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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it