An Analysis of Dampness Study on Heritage Building: A Case Study Ipoh Old Post Office Building and Suluh Budiman Building, UPSI, Perak, Malaysia
Bibliographic record
Abstract
Analysis of dampness covers a variety of methods for measuring moisture content in both high level and trace amounts in solids such as building element. Analysis of dampness in heritage building is very important to avoid serious damage in heritage property. One of the heritages building in Perak is Ipoh Old Post Office Building and the other heritage building in Perak is Suluh Budiman Building at UPSI. Ipoh Old Post Office Building and Suluh Budiman Building are historical buildings which show the colonial’s features and European architecture. From the site observation, a lot of defects occurred in these buildings had been identified during the investigation and some minor defects can be seen in almost every part of the building. High moisture content or dampness at heritage wall had caused almost every part of the building experienced a very serious defect and a prompt action must be taken before the increasing of cost implication. Hence, a study on moisture before conducting any conservation and repair work is needed and it is very important to help the conservator and building environmental designer to analyze and understand the exact sources of dampness’s problem correctly and effectively. The correct analysis of dampness on heritage building will produce the best method to overcome the sources and the actual dampness problem in heritage building generally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".