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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 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.042
Threshold uncertainty score0.233

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 head0.009
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