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Record W2379643348

The Analysis of Evolution Characteristics and Formation Mechanism of Population Distribution in Northeast China

2015· article· en· W2379643348 on OpenAlexaff
GU Guo-fen

Bibliographic record

VenueRenkou yu fazhan · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsScience North
Fundersnot available
KeywordsChinaPopulationEconomies of agglomerationDistribution (mathematics)Population densityGeographySpatial distributionPopulation growthEconomic geographyUrban agglomerationGravity model of tradeDemographyEconomicsEconomic growthMathematics
DOInot available

Abstract

fetched live from OpenAlex

Based on population concentration index,population gravity center model,spatial autocorrelation analysis,this study analyzes the temporal-spatial evolution characteristics of population distribution of 36 cities in Northeast China and discusses the formation mechanism by choosing the years of 1990、2000、2010 and 2013.The analysis results show that;the characteristics of the population distribution in the south of the Northeast China is denser than the north and presents a condition of progressive decrease as a ladder from the south to the north;the density of population grows slowly,and some cities appear negative growth;the population distribution has continually central tendency,the population gravity center moves from northeast to southwest;the population density has strong spatial correlation in each city,the H- H agglomeration areas are mainly focused on the Iiaoning province,the L-L agglomeration areas are mainly concentrated in the Heilongjiang province,and both have a good stability.Sum up,the spatial evolution of population distribution of Northeast China is influenced by the natural environment,the level of economic development,transportation accessibility,historical foundations and policies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.475

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 head0.020
GPT teacher head0.198
Teacher spread0.179 · 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
Published2015
Admission routes1
Has abstractyes

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