New fixed point results in partial quasi-metric spaces
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Bibliographic record
Abstract
Abstract In 1970, D.S. Scott gave applications of Kleene’s fixed point theorem to describe the meaning of recursive denotational specifications in programming languages. Later on, in 1994, S.G. Matthews and, in 1995, M.P. Schellekens gave quantitative counterparts of the Kleene fixed point theorem which allowed to apply partial metric and quasi-metric fixed point techniques to denotational semantics and asymptotic complexity analysis of algorithms in the spirit of Scott. Recently, in 2005, J.J. Nieto and R. Rodríguez-López made an in-depth study of how to reconcile order-theoretic and metric fixed point techniques in the classical metric case with the aim of providing the existence and uniqueness of solutions to first-order differential equations admitting only the existence of a lower solution. Motivated by the aforesaid fixed point results we prove a partial quasi-metric version, when the specialization order is under consideration, of the fixed point results of Nieto and Rodríguez-López in such a way that the results of Matthews and Schellekens can be retrieved as a particular case.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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