From canopy to comfort: The impact of residential landscape choices on thermal dynamics in a semi-arid city
Bibliographic record
Abstract
Extreme summertime heat, intensified by climate change and urbanization, is the leading weather-related cause of mortality in the USA. Cities are increasingly turning to urban forests for heat mitigation, yet the complex relationship between urban ecosystem structure and cooling remains largely uninterrogated, leaving land managers uncertain about how to optimize ecosystem services and minimize disservices. This study examined hyper-local cooling mechanisms in residential landscapes in a semi-arid Colorado, USA city, using data from thirty-two front yards varying by cardinal orientation, ground cover (grass lawns vs. xeriscapes), and canopy cover (0–100 %). Using a mobile biometeorology unit, we measured mean radiant temperature, air temperature, and actual vapor pressure. We found a linear decrease in mean radiant temperature of approximately 1 °C for every 10 % increase in canopy cover at the sub-parcel scale. Xeriscapes were approximately 1.5 °C warmer in morning air temperature than grass lawns. Cardinal orientation effects were inconclusive, questioning the common assumption that trees provide the greatest cooling benefits on the west side of buildings. Findings emphasize the need for context-specific land management recommendations and offer a foundation for further research on how urban structure shapes microclimates at human-relevant scales.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".