Bibliographic record
Abstract
We consider unranked trees that have become an active subject of study recently due to XML applications, and characterize commonly used fragments of first-order (FO) and monadic second-order logic (MSO) for them via various temporal logics. We look at both unordered trees and ordered trees (in which children of the same node are ordered by the next-sibling relation), and characterize Boolean and unary FO and MSO queries. For MSO Boolean queries, we use extensions of the /spl mu/-calculus: with counting for unordered trees, and with the past for ordered. For Boolean FO queries, we use similar extensions of CTL*. We then use composition techniques to transfer results to unary queries. For the ordered case, we need the same logics as for Boolean queries, but for the unordered case, we need to add both past and counting to the /spl mu/-calculus and CTL*. We also consider MSO sibling-invariant queries, that can use the sibling ordering but do not depend on the particular one used, and capture them by a variant of the /spl mu/-calculus with modulo quantifiers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".