Interacting Multiple-Mode Estimation Using Centroid Fixed Structure for High Precision
Bibliographic record
Abstract
Model set design is an important area for multiple-mode estimation, and its main purpose is to design a suitable model set to improve the accuracy and stability of target tacking. In this article, a novel multiple model estimation algorithm, namely, centroid fixed structure of interacting multiple-model estimation (CFIMM), is proposed to obtain the characteristic of the high precision and strong stability. First, the minimum distance method and the minimum model set method are, respectively, provided. Then, based on those two methods, the centroid model set design method is proposed with three different approaches to split the centroid. It proves that the centroid model set has minimal mathematical expectations with the actual models, when the unknown real model space is very large and even uncountable. Finally, the processing of the proposed CFIMM algorithm based on the centroid model set design method is discussed in detail. The proposed CFIMM holds not only the advantages of centroid model set but also the characteristic of the high precision and strong stability. The simulations of CFIMM highlight the correctness and effectiveness of the proposed methods.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".