Bibliographic record
Abstract
Complexity is a very broad subject that applies to project management, engineering design and manufacturing, arithmetic, software, statistics, etc. In maintenance systems, complexity can be defined based on technical and managerial aspects of a maintenance project. Because relative complexity between two projects can be used as a yardstick for resource allocation between them, quantifying the complexity becomes important. To quantify the complexity of maintenance projects, this thesis reports two models. In uncertain situations, a fuzzy graph-based model is developed that determines relative complexities of maintenance projects based on experts‟ opinions with respect to technical and managerial aspects. These aspects may not be measured precisely due to uncertain situations. The model uses an aggregation operator to mitigate conflict of experts‟ opinions on complexity relations. Using a fuzzy relation matrix representing the degrees of membership of relative complexities, the model maps the fuzzy graph into a scaled Cartesian diagram. Also, complexity of a maintenance project can be investigated through time to repair (TTR). Performing statistical analysis shows that human cognition and project complexity have significant influence on TTR. These influential factors can be studied by a learning curve. Due to the nature of maintenance calls for repairs, a learning curve model made up of two segments is proposed. A project complexity can be derived from the learning curve at the breakpoint time. Taking into account human cognitive abilities, the breakpoint indicates the required number of trials in order to reach mastery level for performing certain tasks unsupervised.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".