Bibliographic record
Abstract
A fundamental requirement for cooperating agents is to agree on a selection of component values of objects that can be used for reliably communicating references to the objects, that is, to function as their keys. In distributed environments such as the web, it is more likely that a choice of such values may have time limits on the duration of their ability to serve as keys, e.g., values denoting permissions, authorizations, ser-vice codes, mobile addresses and so on. In this paper, we con-sider how a Boolean complete description logic with a con-cept constructor for expressing “always ” can also be em-bellished with a concept constructor for dynamic or tempo-ral forms of equality generating constraints we call temporal path functional dependencies. In particular, we introduce the temporal description logic DLFDtemp, demonstrate how it can be used, among other things, to capture and reason about temporal keys and functional dependencies for a hypotheti-cal distributed hospital database, and prove that the general membership problem for DLFDtemp is EXPTIME-complete. The latter is accomplished by exhibiting a reduction of the general membership problem for DLFDtemp to the simpler dialect DLF. We also show that the addition of very sim-ple kinds of eventualities leads to a significant increase in the complexity of the membership problem. 1
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".