Evaluating the Effects of Two Energy Retrofit Strategies for Housing on the Wetting and Drying Potential of Wall Assemblies: Summary Report for Year 2007-08 Phase of the Study
Bibliographic record
Abstract
Maintenance of building façades and related systems should be an on-going process. Nonetheless, maintenance prioritisation issues are often neglected due to the lack of available tools to assess vulnerability to climatic effects and susceptibility to deterioration. How is climatic information currently accessed and used and what are the existing means to assess climate loading effects? A Geographic Information System (GIS) platform [ESRI ArcGIS 9.3] was proposed as an appropriate tool to integrate climatic design information and other analyzed climate data in a geographical context and allow users ready access to climate information pertinent to building practitioners. On the basis of this effort climatic design data used for building codes and standards, such as degree days, 15 minute, one day and annual rainfall, driving rain wind pressure and related loads were made available in the GIS platform. This climatic design data provides basic information from which derivative climate parameters can be developed and from which relevant climate load projections can be extracted for use by building designers, practitioners and maintenance experts.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".