Bibliographic record
Abstract
Frequent maintenance and repair programs are crucial to sustain the safety and operability of building facilities. Planning for repairs, however, is a time-consuming and costly task that requires frequent assessment of the condition of all building components. Among the various condition assessment methods that are currently being used, visual inspection can be considered as the most suitable approach for the majority of building components. Visual inspection, however, is highly subjective and requires experienced professionals. As such, the process is time consuming and expensive. There is a need for efficient tools to support the visual inspection process of building components so that it becomes fast, economical, and suitable for less-experienced inspectors. To support the visual inspection process, this paper first identifies the most frequently deteriorated building components and their possible deficiencies. For each of the identified components, a pictorial database is to be developed from a large number of components at different severity levels for the various deficiencies. The pictorial database will then be used to provide guidance for supporting the visual inspection of components, thus making the process faster, less costly, less subjective, and suitable for inexperienced personnel at the individual facility location. The proposed research would aid condition assessment professionals and organizations, such as municipalities and government agencies, to accurately assess the condition of their building facilities to support the repair and fund allocation decisions.
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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".