A special purpose analog computer for statistical system identification
Bibliographic record
Abstract
An iterative method of system identification based on solving the integral equation [Formula omitted] for h(σ) at ten equally spaced points (σ[subscript i] = 1,2,..,10) is described. Replacing the above integral by a finite sum at ten different values of τ results in a set of ten equations in the ten unknowns h(σ[subscript i]). A statistically identical and much more easily handled set of equations, obtained by using one-sample products in place of the actual correlation functions, is then solved by a Gauss-Seidel-like iteration method, the convergence properties of which show this approach to the identification problem to be a useful one for a large class of system input signals. A simple computer to realize the above identification method is described in some detail. The use of a simple quantization form of correlation allows shift registers to carry out the required delay operations. Storage for the computer's estimates of the h(σ[subscript i]) is in the form of step motor driven potentiometers, which also carry out one of the multiplication operations. The very encouraging results of a number of relatively realistic identification tests using the computer are given.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.022 |
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".