Bibliographic record
Abstract
Recently there has been an effort to solve the problems caused by the infamous NULL in relational databases, by systematically applying Kleene's three-valued logic to SQL. The third truth-value is unknown. In this paper we show that by using a fourth truth-value inconsistent, all the advantages of the three-valued approach can be retained, and that negation can be given a constructive, intuitionistic meaning that allows negative knowledge to be specified in the logic explicitly, without having to resort to extra-logical notions of stratification or to non-monotonic reasoning. The four-valued approach also allows for a computationally efficient treatment of query answering in the presence of inconsistencies. This is in contrast to the computationally intractable repair approach to inconsistency management. From a practical view-point we show that the Cylindric Star Algebra, developed by the authors, is particularly well suited for evaluating First Order queries on four-valued databases, and that the framework of data exchange can smoothly adapted to the four truth-values.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".