City-scale energetics: window on adaptive thermal insulation in North American cities
Bibliographic record
Abstract
Abstract Previous research demonstrated that cities are similar to individual mammals in their relationship between the rate of energy use for heating and outdoor air temperature (Ta). At Tas requiring heating of indoor living spaces, the energy-Ta plot of a city contains information on city-wide thermal insulation (I), making it possible to quantify city-wide I by use of the city as the unit of measure. We develop methods for extracting this insulation information, deriving the methods from prior research on mammals. Using these methods, we address the question: in North America, are cities built in particularly cold locations constructed in ways that provide greater thermal insulation than ones built in thermally more moderate locations? Using data for 42 small and medium-size cities and two information-extraction methods, we find that there is a statistically significant inverse relationship between city-wide I and T10-year, the average city Ta over a recent 10-year period (range of T10-year: − 11 to 26 °C). This relationship represents an energy-conserving trend, indicating that cities in cold climates have greater built-in thermal insulation than cities in warm climates. However, the augmentation of insulation in cold climates is only about half as great as would be required to offset fully the increased energy cost of low Tas in a cold climate, and T10-year explains just 5–11% of the variance in measured insulation, suggesting that cities in North America vary greatly in the extent to which thermal insulation has been a priority in city development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".