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Record W2368610762

DIETARY WATER FOOTPRINT OF URBAN AND RURAL RESIDENT IN JILIN PROVINCE

2013· article· en· W2368610762 on OpenAlexaff
Tang Zhen-zhe

Bibliographic record

VenueYunnan dili huanjing yanjiu · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsScience North
Fundersnot available
KeywordsFootprintGeographyEcological footprintConsumption (sociology)Rural areaIndex (typography)UrbanizationSocioeconomicsFood consumptionSignificant differenceEnvironmental protectionAgricultural economicsEcologyBiologyMathematicsSustainabilityStatisticsMedicineEconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

Daily dietary consumption is closely linked to water resources protection. According to the dietary consumption per urban and rural resident in Jilin province in 1999- 2011,the dietary w ater footprints are calculated,and its characteristics are analyzed. The result show s the fluctuation of the dietary w ater footprint per urban and rural resident in Jilin province in 1999- 2011 is not significant,but there are differences betw een the dietary w ater footprint of urban and rural resident. The w ater footprint ratio of meat is the highest for urban resident,w hile the w ater footprint ratio of grain is the highest for rural resident. Diversity index of dietary w ater footprint for urban resident is higher than rural resident,and both are increasing,especially rural resident's increases fast,w hich show s the w ater footprint of dietary types tend to be more and more balanced,w hereby the types of dietary consumption are constantly rich. The inequality index of dietary w ater footprint per urban and rural resident show s that there exists difference betw een the dietary w ater footprint of urban and rural resident,w hile the difference is not big,that is to say,the unfair is not obvious.

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)0.000
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 score0.045
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.183
Teacher spread0.164 · 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
Published2013
Admission routes1
Has abstractyes

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