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Record W3124475224 · doi:10.26577/jgem.2020.v59.i4.01

Qualitative condition of agricultural lands of the Turkestan region

2020· article· en· W3124475224 on OpenAlexaboutno aff
A. Zh. Tazhekova, Aigul Тokbergenova, Kanat Zulpykharov

Bibliographic record

VenueJournal of Geography and Environmental Management · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureArable landAgricultural landGeographyEuropean unionLand useLand managementLand developmentSustainable developmentEnvironmental planningAgricultural economicsNatural resource economicsEnvironmental resource managementBusinessPolitical scienceEconomicsInternational tradeEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The article discusses the qualitative state of agricultural land in the Turkestan region. Using statistical data in recent years, the dynamics of changes in the areas of arable land is shown, negative factors affecting the quality of agricultural land are identified.. The main factor in the degradation of agricultural land in the region is erosion. The article identifies priority areas for the effective use of agricultural land, taking into account regional peculiarities of the region and outlines the issues of improving their effective use.The article discusses the three main structures for the sustainable development of agriculture and the agro-industrial complex, which are important in the rational management of agriculture and land resources. Analyzed the European version of the development of agricultural land and agriculture in Europe under the program of the European Union ERA-NET. The experience of developed countries such as Australia, Canada, Russia is considered and recommendations are given on improving the quality of agricultural land in the Turkestan region.

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 score0.319
Threshold uncertainty score0.092

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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
Published2020
Admission routes1
Has abstractyes

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