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
An open issue in Adaptive IIR Filtering (AIF) is that of convergence to a global minimum in the presence of observation noise, when the system is insufficiently modeled, or when the excitation source is colored. It is well known that algorithms based on Equation Error (EE) contain a single minimum that may be biased whereas, algorithms based on Output Error (OE) ensure the existence of an unbiased global minimum in presence of local minima. Recently, there have been a number of attempts to combine these formulations in order to ensure the existence and uniqueness of an unbiased minimum. The work presented here, Equation Error Output Error (EEOE) and Modified EEOE (MEEOE,) are such attempts in the context of system identification. Although the formulation of EEOE did not achieve the desired outcome and was later found out to be similar to that proposed by Kenny and Rohrs (1993), the exploration of its limitations, however, led to a superior algorithm namely, MEEOE.
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 | 0.000 | 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) | 0.003 | 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