Maximizing the pedestrian radiative cooling benefit per street tree
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
Outdoor heat stress is a growing problem in cities during hot weather. City planners and designers require more pedestrian-centered approaches to understand sidewalk microclimates. Radiation loading, as quantified by mean radiant temperature (TMRT), is a key factor driving poor thermal comfort. Street trees provide shade and consequently reduce pedestrian TMRT. However, placement of trees to optimize the cooling they provide is not yet well understood. We apply the newly-developed TUF-Pedestrian model to quantify the impacts of sidewalk tree coverage on pedestrian TMRT during summer for a lowrise neighbourhood in a midlatitude city. TUF-Pedestrian captures the detailed spatio-temporal variation of direct shading and directional longwave radiation loading on pedestrians resulting from tree shade. We conduct 190 multi-day simulations to assess a full range of sidewalk street tree coverages for five high heat exposure locations across four street orientations. We identify street directions that exhibit the largest TMRT reductions during the hottest periods of the day as a result of tree planting. Importantly, planting a shade tree on a street where none currently exist provides approximately 1.5–2 times as much radiative cooling to pedestrians as planting the same tree on a street where most of the sidewalk already benefits from tree shade. Thus, a relatively equal distribution of trees among sun-exposed pedestrian routes and sidewalks within a block or neighbourhood avoids mutual shading and therefore optimizes outdoor radiative heat reduction per tree during warm conditions. Ultimately, street tree planting should be a place-based decision and account for additional environmental and socio-political factors.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it