Causal Effects between Criteria That Establish the End of Service Life of Buildings and Components
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
In the last decades, considerable work has been done regarding service life prediction of buildings and building components. Academics and members of the CIB W080 commission, as well as of ISO TC 59/SC14, have made several efforts in this area and created a general terminology for the concept of service life, which is extremely relevant for property management, life cycle assessment (LCA) and life cycle costs (LCC) analyses. Various definitions can be found in the literature that share common ideas. In fact, there are different criteria that trigger the end of a building’s service life, but the trap that building practitioners too often fall into and that should be avoided is dividing a problem into separate boxes, labels, and specializations without the mutual cohesion and interaction, and ignoring human behavior. Some definitions of service life are discussed in this review paper, in which the cause-effect processes underlying aging and decay are described. These descriptions highlight the continuous interrelation between different criteria for the end of a building’s service life, considering too often neglected and misunderstood causes of the end of life.
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 | 0.001 | 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 it