Bibliographic record
Abstract
Workflow m anagement is crucial in monitoring and controlling business processes. Advancements have been made in computer-based workflow management, which allowed for the partial or complete automation of these processes. However, existing computer-based systems have not been successful in highly distributed domains. Hence, these domains have continued conducting their workflow management processes in an adhoc manner. Conducting workflow management in this way has three flaws: human dependency, confined knowledge, and inconsistency in tasks. Metis, an event-based workflow service, was developed to alleviate these flaws by bringing automation to workflow management in highly distributed domains, primarily digital libraries. This project conducted an evaluation of Metis using two data retrieval processes. Each workflow carried out via Metis soundness using a petri net analysis; in addition performance analysis was also conducted. Soundness tested Metis’ ability to handle exceptions and/or errors that may occur. The performance analysis is a qualitative measurement based on two major metrics: quality and efficiency. This evaluation found that while Metis’ approach is valid it needs further modification in areas requiring the automation of repetitive tasks such as multiple file transfers. Furthermore, the qualitative analysis showed an improvement in conducting workflow management using Metis versus in an adhoc manner.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".