A Methodology to Ensure the Consideration of Flexibility and Robustness in the Selection of Facility Renewal Projects
Bibliographic record
Abstract
Facilities are built to provide an adequate level of service over long periods of time. During these long periods, the required levels of service from facilities can change significantly, and these changes cannot be predicted with certainty. Having facilities that can be easily modified, or whose use can be easily modified, can increase the net benefit of facilities for owners over these long periods of time. This flexibility and robustness, respectively, should be adequately taken into consideration when designing and maintaining facilities, along with the myriad of possible futures that may occur and their associated probabilities of occurrence. In this paper, a systematic methodology is proposed that facility managers can use to identify possible changes in the required levels of service of facilities over specified time periods, to generate possible renewal projects to execute now, and to evaluate these. The methodology is demonstrated by using it to determine possible projects to change a military barracks, to make it easier to use the barracks to accommodate future changes in the required amounts of space, and determining which of these yields the highest net benefit.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".