Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Value engineering (VE) frequently is applied to construction projects for better project scope recognition and for elimination of unnecessary cost without affecting the functional requirements of individual components of constructed facilities. A critical phase in the application of value engineering is the evaluation of generated alternatives based on the defined criteria for that purpose. Limited work has been carried out for the automation of this process yet without adequate visualization for the components being considered. This paper presents an automated model for design professionals, owners, and members of VE teams to evaluate and compare different design alternatives of project components using multiattributed criteria, as well as integrating that model with visualization capabilities to assist designers and stakeholders in making related decisions. The analytic hierarchy process (AHP) is used to develop a multiattributed decision support model for evaluating competing alternatives. The model is then integrated with BIM to provide visualization capabilities and assist in cost estimating of the project components being considered. A prototype model that integrates the project BIM with RS Means cost data and AHP has been developed. The model has been applied to a case project and evaluates and ranks generated alternatives in its output report.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it