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
Construction material use causes about 11% of global GHG emissions and is an accelerating driver of global warming. In this research, we use image-based machine learning to predict the floor area and age of buildings which are strongly correlated with embodied GHG emissions. The ability to automatically estimate building attributes from street view images can enable large-scale analysis of the built environment and provide better differentiability compared to patch-wise or pixel-wiseestimation from satellite images. A ResNet-18 model is used for feature extraction, and area and age predictions are formulated as a regression problem and a classification problem, respectively. On area prediction, our model achieves a Mean Absolute Percentage Error of 22.32%. On age prediction, our model achieves a Balanced Accuracy (BA) of 78.05% and Accuracy of 79.05% when there are 3 age classes, but the BA and Accuracy drop to 61.94% and 63.53%, respectively when there are 6classes.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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