Bibliographic record
Abstract
Summary form only given. One philosophically coherent position is to view system (or process) identification as the search for a theory (or model) in a given class that minimizes a (loss) function of (1) the cumulative prediction errors incurred using a particular model and (2) a measure of the complexity of the model (such as the McMillan degree of a linear predictor). The resulting identification method is referred to as a minimum-predictor-error (MPE) method. An alternative starting point taken in the minimum-description-length (MDL) theory due to Rissanen is to view a process or predictor model as an encoding device and to choose the model (in a given class) that minimizes the total number of bits needed to describe (1) the model plus (2) the number of bits required to describe the observations when encoded using the model. An extension of this idea is contained in Rissanen's stochastic complexity (SC) measure of a process. The author has related the MPE, MDL, SC and classical maximum-likelihood approaches to system identification.>
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".