Bibliographic record
Abstract
In artificial intelligence there is a great need to represent temporal knowledge and to reason about models that capture change over time. Change seems to be constant in a continuously changing world. In many domains such as science, medicine, finance, and demographics, change is noticeable from one time to another. The thesis work aims at first extending FCA to capture temporal evolutions (TFCA) represented by concept lattices in time-stamped databases, and at applying the extended FCA techniques to data mining with an endeavor of inferring temporal properties. Extending formal concept analysis to temporal domains allows us to use concept lattices to visualize temporal evolutions and deduce insights on the hidden regularities in the data. To represent temporal evolutions, formal entities are time indexed. Temporal edges are added to concept lattices to show evolutions. Important temporal properties such as class evolution, persistence, and transition are classified and a mechanism for inferring them is presented. Algorithms for inferring temporal properties and generating temporal lattices from time-stamped databases are developed, implemented, and tested.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .N46. Source: Masters Abstracts International, Volume: 40-06, page: 1551. Advisers: Richard A. Frost; Ahmed Y. Tawfik. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".