MULTIPLE MODEL CONTROL IMPROVEMENTS: HYPOTHESIS TESTING AND MODIFIED MODEL ARRANGEMENT
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
This work demonstrates the fusion of two concepts in switching systems, namely, hypothesis testing and multiple model adaptive control. A hypothesis test switching method is defined to detect parameter jumps in a stochastic environment and select new models. The control of discrete-time systems with rapidly time-varying parameters is simulated. Hypothesis test switching is compared to the most frequently researched performance index switching method. The proposed method is found to be unique because it achieves lower control error and operates without user adjustment or a priori knowledge of parameter behaviour and model placement. In addition, a modification to the way multiple models are arranged is proposed. Using the modified arrangement, performance increases are demonstrated, stability is proven more easily, previously required assumptions can be relaxed, and new switching methods can be applied.
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 | 0.001 | 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