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Record W3200914388 · doi:10.4018/ijagr.2021100101

Satellite and Ground Estimates of Surface and Canopy-Layer Urban Heat Island

2021· article· en· W3200914388 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Applied Geospatial Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsMcGill University
Fundersnot available
KeywordsDaytimeUrban heat islandTemperate climateUrbanizationGeographyEnvironmental scienceClimatologyMonsoonAridClimate changeAtmospheric sciencesMeteorologyOceanographyEcologyGeology

Abstract

fetched live from OpenAlex

The urban heat island (UHI) effect is one of the prominent impacts of urbanization that affects human health and energy consumption. As the data is limited and inconsistent, UHI comparative studies between UHIUCL and UHISurf on the seasonal scale are limited. The use of only daytime summer imagery reporting “Inverted UHI” undermines the holistic view of the phenomenon. Therefore, this study analyses the seasonal patterns for UHISurf and UHIUCL in three climate zones (Delhi, Pune, and Montreal). The three cities experience a high traditional night-time UHIUCL (Delhi 7°C, Pune 6°C, Montreal 1.89°C). Landsat captures a prominent daytime UHISurf (15°C) in Montreal with temperate climate and daytime inverted UHISurf (-4°C) for Delhi in summer. Seasonally, the night-time UHI is prominent in summer and monsoon for Delhi, summer and spring for Pune, and summer for Montreal. Due to UHI effect, the heatwaves can be more intense in semi-arid and tropical cities than temperate cities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.318
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it