Full disjunctions: polynomial-delay iterators in action
Bibliographic record
Abstract
Full disjunctions are an associative extension of the outerjoin operator to an arbitrary number of relations. Their main advantage is the ability to maximally combine data from different relations while preserving all the original information. An algorithm for efficiently computing full disjunctions is presented. This algorithm is superior to previous ones in three ways. First, it is the first algorithm that computes a full disjunction with a polynomial delay between tuples. Hence, it can be implemented as an iterator that produces a stream of tuples, which is important in many cases (e.g., pipelined query processing and Web applications). Second, the total runtime is linear in the size of the output. Third, the algorithm employs a novel optimization that divides the relation schemes into biconnected components, uses a separate iterator for each component and applies outerjoins whenever possible. Combining efficiently full disjunctions with standard SQL operators is discussed. Experiments show the superiority of our algorithm over the state of the art. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".