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An Overview of Karst Ecosystem in Southwest China: Current State and Future Management

2015· article· en· W2233278645 on OpenAlexaff
Cao Jianhua, Tong Liqiang, Azim U. Mallik, Yang Hui

Bibliographic record

VenueJournal of Resources and Ecology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsLakehead University
Fundersnot available
KeywordsDesertificationKarstChinaPovertyGeographyVegetation (pathology)EcosystemSoil conservationSustainabilityRestoration ecologyEcosystem servicesSustainable developmentEnvironmental protectionWater erosionEnvironmental resource managementEnvironmental scienceWater resource managementEcologyAgriculturePolitical scienceSoil water

Abstract

fetched live from OpenAlex

Karst areas in Southwest China, with Guizhou as the focal center, are confronted with ecological deterioration and large areas of rocky desertification. Human activities are defined as the driving force behind the soil erosion. Further, local farmers in the area suffer from poverty due to a lack of drinking water, food and a weak living environment. Over one-third of national poverty-stricken counties occur in this part of China. To balance ecological protection and economic development in the region and help local farmers out of poverty we propose integrated controls and discuss on ground water exploration and sustainable use, soil conservation and remediation, and vegetation restoration (especially economic plant species) in this paper.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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

Citations63
Published2015
Admission routes1
Has abstractyes

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