A Blockchain-based Approach to Support an ISO 9001:2015 Quality Management System
Bibliographic record
Abstract
Quality is an essential element for any company that wants to be recognized successfully; because of this, the companies undergo a certification process under a quality standard such as ISO 9001 that allows them to improve the performance of its operation, differentiate itself from its competitors, achieve a better position in the market and export quickly. In this sense, making and maintaining such certification can be under high pressure over the company, up to become a source of corruption risk, since, in emerging markets, companies may be tempted to perform unethical practices such as falsifying or adulterating documents to maintain their certifications and the benefits derived from it. Besides, considering that the quality management system audit process is based on the verification made by a third party of the documents and records of the company against the quality standard, it becomes necessary to reinforce with technology the audit process to minimize this kind of risk. Given this, a software architecture for a quality management system supported in BPMN and Blockchain technology is proposed to guarantee the integrity and immutability of the system and information, which allows exposing any attempt at fraud and facilitates the audit process's automation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".