Spatial and Temporal Variations in Land Surface Temperature as an Indicator of Learning Comfort Level in Banten Province
Bibliographic record
Abstract
Increasing population and rapid development of built-up land can cause an increase in land surface temperature. These changes are often associated with many factors, including changes in land use, and surface parameters, economic development, and vegetation. This research aims to analyze the temperature factor as an indicator of indoor thermal comfort to support the learning process in schools in the Banten Province area. This research uses a spatial temporal approach with descriptive statistical analysis. Based on the results, show that the land surface temperature at each school location in Banten province shows conditions that are not constant. The number of schools with uncomfortable conditions varies from 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), and 2023 (52.44%). This shows that there has been a decrease in uncomfortable conditions for studying by 36.63% in the last 5 years.
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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 | 0.001 | 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".