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Record W2889141184 · doi:10.29007/qp7s

Object-Core Oriented Data Modelling for Tracking of Behaviors of Urban Heat Islands

2018· paratext· en· W2889141184 on OpenAlexaff
Rui Zhu, Éric Guilbert, Man Sing Wong

Bibliographic record

VenueEasyChair preprint · 2018
Typeparatext
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversité Laval3v Geomatics (Canada)
Fundersnot available
KeywordsThematic mapUrban heat islandTracking (education)Object (grammar)Period (music)Computer scienceGeographyCartographyClimatologyMeteorologyArtificial intelligenceGeologyPhysicsSociology

Abstract

fetched live from OpenAlex

Modeling thematic and spatial dynamic behaviors of Urban Heat Islands (UHIs) over time is crucial to understand the evolution of this phenomenon and the city micro-climate. Previous studies conceptualized that a UHI can only have a single life period with spatial behaviors (i.e. areal changes and topological transformations). However, a UHI can also appear and disappear periodically several times expressed by thematic and spatial integrated behaviors, which has not been established yet. Thus, this study conceptualizes each UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life-time transitions triggered by the behaviors. The model was implemented in an object-relational database, and air temperatures collected from a number of weather stations were interpolated as temperature images each hour for six weeks. Results indicated that the model could track the spatial and thematic evolution of UHIs through continuous time effectively, and also revealed the periodical patterns and abnormal cases of UHIs over a city.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.299
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2018
Admission routes1
Has abstractyes

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