Bibliographic record
Abstract
In Chapter 3 on Metrology, the measurement concepts have been presented as defi ned in classical metrology and used throughout the sciences and in engineering.However, software has defi ned its own measurement terminology; for instance, while the widely " quantity, " " base quantity " and " derived quantity " are the terms adopted in metrology, the corresponding terminology in software would be " attribute, " " base attribute " and " derived attribute."To help practitioners and researchers in the software engineering and metrology communities bridge the cultural gap between them, this Appendix presents the terms, and their defi nitions, that would be the most familiar to each culture, while not specifi cally recommending their use. B.1 SOME MEASUREMENT TERMS IN SOFTWARE ENGINEERINGAttribute: the property of an entity that can be determined quantitatively, that is, for which a magnitude can be assigned.In the metrology vocabulary, this is called a measurable quantity , or quantity for short. 299Some quality attribute models, such as in ISO 9126, use the terms quality characteristic and sub -characteristic.ISO 15939 considers an attribute to be a property related to information needs.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.418 | 0.303 |
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".