Bibliographic record
Abstract
This is a reissue of a paper which appeared in ACTA IMEKO 1988, Proceedings of the 11th Triennial World Congress of the International Measurement Confederation (IMEKO), "Instrumentation for the 21st century", 16.-21.10.1988, Houston, pp. 337-345. After a review and description of current trends in the design of electronic measurement and analytical instrumentation, changes in its application and use, and of associated quality issues, this paper deals with new quality issues emerging from the expected increase of artificial intelligence impact on system design and implementation strategies. The concept of knowledge quality in all its aspects (i.e. knowledge levels, representation, storage, and processing) is identified as the key new issue. Discussion of crucial knowledge quality attributes and associated assurance strategies suggests the need to enrich the assurance sciences and technologies by the methods and tools of applied epistemology. Described results from current research and investigation, together with first applications of artificial intelligence to particular analytical instruments, lead to conclusion that the conceptual framework of quality management is, in general, adequate for successful resolution of all quality issues associated with intelligent instrumentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".