A Semantic Knowledge Management Environment for Product Life Cycle Costs
Bibliographic record
Abstract
There is an increasing need for effective tools for managing life cycle costs of civil products. These products range from large infrastructure systems, such as bridges and highways, to smaller items such as HVAC systems. The industry has expressed a need for collaborative systems for optimizing product LCC and for incorporating industry best practice into the optimization process. This paper presents a web-based semantic system for managing products' life cycle costs. The basic architecture of the proposed system represents costs as a hierarchy of cost elements. Each cost element has a dollar value that could be deterministic, probabilistic or fuzzy. Several indigenous and exogenous factors (also represented in hierarchies) can have a set of impacts on the values of these costs. Through the analysis of different impact possibilities and probabilities, a decision maker can study various alternative scenarios and define the optimum set of costs and their values. A set of web services are used to capture cost elements, factors and their impacts. A schema for representing industry knowledge regarding costs and factors that influence their performance. The semantic nature of the system allows for it to be an integral part of a corporate memory system, where decision makers will be able to document and access lessons learned about LCC optimization.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".