MétaCan
Menu
Back to cohort
Record W2390357775

Spatial Distribution of Traditional Villages and the Influencing Factors in Hunan Province

2015· article· en· W2390357775 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsScience North
Fundersnot available
KeywordsGeographyDistribution (mathematics)SocioeconomicsScale (ratio)Spatial distributionEnvironmental protectionEconomic geographyCartographySociology
DOInot available

Abstract

fetched live from OpenAlex

For the reasonable exploitation and protection of traditional villages, this article is based on the spatial analyst tools, analyzing spatial distribution characteristics and influencing factors of the 103 traditional villages in Hunan Province. Research shows that the structural types of traditional villages in Hunan Province were identified agglomerate.The distribution of the traditional villages is comparatively centralized from the cities scale. The traditional villages are concentrated in Western Hunan Tujia Nationalities Autonomous Prefecture, Chenzhou, Yongzhou, Huaihua and Shaoyang. The centralized distribution of the traditional villages in these areas was inequality. The highest concentrations are in Western Hunan Tujia Nationalities Autonomous Prefecture, the next is southern Hunan areas. The factors that affect the distribution of traditional villages are relatively closed regional environment, perilous hills, inconvenient transportation and relatively backward economic which are essential to the traditional villages protection.

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)0.000
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 score0.021
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.203
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

Quick stats

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicRural development and sustainabilityFrench-language works237,207