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Record W2384351626

Relationship Between Urban Thermal Distribution and NDVI of Shanghai City

2007· article· en· W2384351626 on OpenAlexaff
Rui Jian-xun

Bibliographic record

VenueJournal of Jishou University · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsNormalized Difference Vegetation IndexUrban heat islandDistribution (mathematics)Spatial distributionRemote sensingRing roadEnvironmental scienceGeographyPhysical geographyMeteorologyMathematicsGeologyClimate changeArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Vegetation can adjust the urban thermal distribution,and there is a consanguineous connection about distributing and intensity of urban thermal distribution and NDVI.The research of the relationship between urban thermal distribution and NDVI will find their spatial distributing characters and reflect the change of urban spatial structure and urban entironment.Base on the softwares ARCGIS and ARCVIEW,use spacial analyst technique of GIS,calculate and distill information about urban thermal distribution and NDVI which locate the area within outside ring road of Shanghai City from TM images of Shanghai,2003,build the regression approach equation of urban thermal distribution and NDVI,and do analysis on the relationship between urban thermal distribution and NDVI.The result indicates that the relationship between urban thermal distribution and NDVI is minus correlation.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.135

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 head0.025
GPT teacher head0.209
Teacher spread0.184 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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