Spatiotemporally characterizing urbantemperatures based on remote sensing and GISanalysis: a case study in the city of Saskatoon(SK, Canada)
Bibliographic record
Abstract
Abstract The purpose of this study is to spatiotemporallyexplore the characteristics of urban temperaturesbased on multi-temporal satellite data and historical insitu measurements. As one of the most rapidly urbanizedcities in Canada, Saskatoon (SK) was selected as our studyarea. Surface brightness retrieving, Pearson correlation,linear regression modeling, and buffer analysis were appliedto different satellite datasets. The results indicatethat both Landsat and MODIS data can yield pronouncedestimations of daily air temperature with a significantlyadjusted R2 of 0.803 and 0.518 at the spatial scales of 120mand 1000 m, respectively. MODIS monthly LST data ishighly suitable for monitoring the trend of monthly urbanair temperature throughout summer (June, July, and August)due to a high average R2 of 0.8 (P<0.05), especiallyfor the warmest month (July). Our findings also reveal thatboth the Saskatchewan River and urban green spaces havestatistically significant cooling effects on the surroundingurban surface temperatures within 500 m and 200 m, respectively.In addition, a multiple linear regression modelwith four influential factors as independent variables canbe developed to estimate urban surface temperatures witha highest adjusted R2 of 0.649 and a lowest standard errorof 0.076.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".