Bibliographic record
Abstract
Object-oriented programming language has gained popularity in recent years. However, some problems exist in object-oriented programming languages. It works well with static classification, but does not support object dynamic classification. Static classification means an object always and only belongs to one class during its life spans. In real-world applications, objects may belong to different classes rendering different roles certain times during the lifetime. Dynamic classification enables the changing of object classification over time. Objects can be classified and declassified into/from acquire and release class membership during runtime. In this thesis, many approaches to dynamic classification will be discussed in different implementing languages. Based on the thorough reviews of these approaches, we give a new approach. This approach combines the concept of object and roles and extends a class hierarchy with dynamic classification. The syntax of dynamic classification shows how to implement the function of dynamic classification in the object-oriented programming language. Finally, we present a preprocessor, by which a C♯ code including the extendable dynamic classification functions can be translated to standard C♯ code.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W364. Source: Masters Abstracts International, Volume: 43-03, page: 0892. Adviser: Liwu Li. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".