A Strategy for Partial Evaluation of Views
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Database applications and environments such as mediation over heterogeneous database sources and data warehousing for decision support lead to complex queries. Queries are often nested, defined over views, and may involve unions. In certain cases, one might want to “remove” pieces (sub-queries or sub-views) from such queries. Some sub-views may be effectively cached, or may be materialized views. Some may be known to evaluate empty, through reasoning over the integrity constraints. Some may match protected queries, which for security cannot be evaluated. We introduce an evaluation strategy called tuple-tagging for queries defined over views that efficiently “removes” marked sub-views. This differs from the approach of rewriting the query so that the sub-views to be removed are effectively gone, and then evaluating the rewritten query. With the tuple tagging evaluation, no rewrite of the original query is necessary.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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