Dialectical Logic K-Model: the Discrete Time Dynamical Sampling System, Multidimensional Logic Variable and Associate Database(ADB)
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
Following the earlier works about dialectical logic K-model by the author, in this succeed paper author described the three problems : the first is that discrete time dynamical sampling system to solve which the true-value function is unknown and need discrete time dynamical sampling system to obtain a series of sampled discrete time true-value function points to predict the continuous true-value function or we need some properties of true value function in the frequency domain, a several formulas for true-value function of single-dimensional logic variable via discrete Fourier transformation are explained; the second is the graph expression and matrices expression for the multidimensional logic variables in dialectical logic K-model. Multidimensional logic variable is important that can be used in multidimensional contradictions and in multiple-person games. In fact, author also described the graph $G_K^p$ and corresponding matrices of the multidimensional logic variables; the third is the machine oriented database, associated database i.e. ADB, this is a new database for artificial intelligence, in present paper author describes theoretical properties and some features of ADB.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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