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Bibliographic record
Abstract
The author has granted a nonexclusive licence allowing the National Lïbrary of Canada to reproduce, loan, distriiute or sell copies of this thesis in microfom, paper or electronic formats. The author retains ownership of the copyright in this thesis. Neither the thesis nor substantial extracts fiom it may be printed or othewise reproduced without the author's permission. L'auteur a accordé une licence non exclusive permettant à la Biblbthèque nationale du Canada de reproduire, prêter, distribuer ou vendre des copies de cette thèse sous la forme de microfiche/film, de reproduction sur papier ou sur format électranique. L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la thése ni des extraits substantiels de celle-ci ne doivent être imprim6s ou aumement reproduits sans son autorisation. A Graph-oriented Query Language for Semi-Structured Data: Theoreticai and Practical Analysis This study examines the theoretical foundations and the practical aspects of the graph-oriented query language for the semi-structured data (SSD) proposed in wS99]. SSD is a data model that is designed for heterogeneous data sources. It allows for information integration and information sharing over the Intemet. Several query languages for the SSD model have been proposed but none has been standardized yet. This paper analyzes the graph-oriented query language proposal, and suggests ways in which it can be Mer improved to fit the SSD model. First and foremost, 1 would Like to thank rny supervisor, Professor Goste Grahne, for taking me on as a mident about two years ago, even though he knew little about me. He has been a role mode1 to me in his dedication and professionalism-My choice of career has been greatly influenced by Mr Grahne and I hope that 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.033 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.693 | 0.683 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".