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
Building performance simulation in extreme conditions is crucial for improving the resilience of buildings to withstand climate change-induced weather events. Using Actual Meteorological Year weather files instead of Typical Meteorological Year files allows for accurate estimation of building performance during such extreme conditions, enabling the assessment of vulnerabilities and areas that require improvement. The approach applies to both existing buildings needing climate change-resilient retrofits and new building designs that must be compatible with future climatic conditions. The intensification and frequency increase of these extreme weather events make developing adaptation and resilient-building measures imperative, involving understanding potential losses households may experience due to the intensification of extreme events. Addressing the knowledge gap caused by the absence of an AMY weather file dataset is essential for accurate BPS during past extreme climate change-induced weather events. This article introduces a comprehensive .epw format weather file dataset focusing on historical extreme weather events in Canada, encompassing a diverse array of past occurrences in various locations, allowing for better estimation of thermal performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.638 |
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