Stone Soup: announcement of beta release of an open-source framework for tracking and state estimation
Bibliographic record
Abstract
Tracking and state estimation technologies are used in a variety of domains that include astronomy, air surveillance, maritime situational awareness, biology, and the internet. Algorithms for tracking and state estimation are becoming increasingly complex and it is difficult for researchers and skilled practitioners to implement and systematically evaluate these state-of-the-art algorithms. System designers also need to objectively assess the performance of algorithms against operational requirements, and tools to conveniently perform such systematic assessment have been lacking. Recognising this problem, an initiative was taken to create an open-source frame- work called Stone Soup", which would be used for the development, demonstration, and evaluation of tracking and state estimation algorithms. Stone Soup was made openly available in April 2019 as a beta version (V0.1b1). This paper introduces the Stone Soup framework and describes how users can take advantage of this framework to develop their own algorithms, set up experiments with real-world data, and evaluate algorithms.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.031 |
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".