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Record W2768698293 · doi:10.5539/jsd.v10n6p214

Evaluation of Sustainability of Zhengzhou’s Land Use

2017· article· en· W2768698293 on OpenAlexvenueno aff
Nan Wang, Shenghui Li

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityLand useAsset (computer security)ReproductionEnvironmental resource managementIndex (typography)Resource (disambiguation)PopulationBusinessGeographyNatural resource economicsEconomicsEcologyComputer science

Abstract

fetched live from OpenAlex

Land is not only a major space for human production and living, but also one of the most precious resources to humans. As a space carrier of urban construction, urban land resources constitute the part with the highest asset benefit among land resources, offering an essential space for economic reproduction, population reproduction and environment reproduction in urban areas. To sum up, urban land resources are the material basis, guaranteeing sustainability of urban development.In this paper, changes of sustainability of land use in Zhengzhou City, Henan Province from 2011 to 2015 were analyzed so as to evaluate sustainability level of land use in Zhengzhou. Based on correlation analysis, resource, environment, economy and society were selected as four evaluation indexes, and their weights were determined. Then, the method of maximum was used to realize data normalization, and the comprehensive index value was computed. Finally, sustainability of Zhengzhou’s land use was comprehensively evaluated. Taken as a whole, sustainability of Zhengzhou’s land use was improving from 2011 to 2015, but the comprehensive sustainability level was still low, calling for further strengthening. From 2014 to 2015, the sustainability level of land use was still on the downward.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.143
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.271
Teacher spread0.234 · 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
Published2017
Admission routes1
Has abstractyes

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