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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".