DISIMA Distributed Image Database System: Experiences and Reflections
Bibliographic record
Abstract
DISIMA (Distributed Image Database Management System) is a research project under development at the University of Alberta that explores: (i) the development of an object-oriented DBMS kernel that provides flexibility for user-defined classification of images, supports feature-based and spatial querying over image content, and enables reasoning over spatial relationships for query optimization; (ii) the development of query languages and primitives for querying image databases; and (iii) the provision of scalability and open access to image repositories. A prototype system is implemented. This paper presents an overview of the project and reflects on our development experiences in the context of our related research. 1 Introduction DISIMA (Distributed Image Database Management System) is a research project under development at the University of Alberta. The research topics under investigation include: (i) the development of an object-oriented DBMS kernel that provides flexibili...
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".