UNIVERSITY OF ALBERTA The Design and Implementation of TIGUKAT User Languages BY
Bibliographic record
Abstract
To meet the data management requirements of new complex applications object management sys tems are emerging as the most likely candidate The general acceptance of this new technology de pends on the increased functionality it can provide and one measurement is the power of its query model Users of these systems must have a declarative language to formulate queries on what information is required without specifying how to e\tciently retrieve the information Therefore the formal query model should dene a declarative calculus that can be used to formulate queries to the objectbase and an equivalent procedural algebra to execute them e\tciently In addition a userlevel language should be provided which has the same expressive power as the formal languages This thesis presents the new TIGUKAT Language that was designed and implemented within the framework of the TIGUKAT project It is a high level user language which provides declarative access to the underlying objectbase It is divided into three parts TIGUKAT Denition Language TDL TIGUKAT Query Language TQL and TIGUKAT Control Language TCL The syntax of this language and the main design choices where in\nuenced by SQL while the semantics is dened
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".