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Record W2020797968 · doi:10.5539/jas.v3n1p163

Analysis on the Evolution of Urban Land Structure and Economic Driving Force in Changde

2011· article· en· W2020797968 on OpenAlexvenueno aff
Jun Xiao, JIAN-Qiang Li, Jianglong Chen, Peng Tang

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Quality and Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationDominance (genetics)Entropy (arrow of time)GeographyEconomic geographyEconomicsEconometricsEnvironmental scienceEconomic growthPhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Based on information entropy theory, the dynamically evolutional characteristics of urban land structure were analyzed in the time and space from 2001-2008, in Changde city. In time, the entropy and equilibrium of urban land information increased first and then decreased, with a slight fluctuation in 2007. The overall entropy and equilibrium showed a relatively stable trend, but the degree of dominance and equilibrium were the opposite trend. In space, information entropy, degree of equilibrium and dominance showed significant differences in each designated town of Changde. Further by constructing a multiple regression model to investigate economic driving force of the urban land evolution in Changde city, this paper found that the level of urbanization was the most important economic driver, followed by investment in fixed assets and GDP.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.195
Teacher spread0.187 · 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 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
Published2011
Admission routes1
Has abstractyes

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