Interacting Multiple-Mode Estimation Using Centroid Fixed Structure for High Precision
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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