Dialectical Logic K-Model: the Discrete Time Dynamical Sampling System, Multidimensional Logic Variable and Associate Database(ADB)
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".