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Monitoring of the State of Agricultural Lands of the Rostov Region and the Effectiveness of Their Use

2023· article· en· W4367682037 on OpenAlexaboutno aff
Alexander A. Kalinin, Natalia V. Ponomarenko, Zhanna V. Matveikina

Bibliographic record

VenueUNIVERSITY NEWS NORTH-CAUCASIAN REGION NATURAL SCIENCES SERIES · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureZoningNatural resourceQuarter (Canadian coin)GeographyAgricultural landAgricultural economicsGeopoliticsAgricultural productivityVolga regionTourismSunflowerEnvironmental protectionAgroforestryPolitical scienceEconomicsArchaeologyAgronomyEnvironmental science

Abstract

fetched live from OpenAlex

The Southern Federal District is the most important geopolitical, strategic, economic and agricultural region of Russia. The south of our country is the center of agricultural production. It is here that the largest suitable for cultivation and the most fertile lands are concentrated, on which up to a quarter of the harvest of grain crops and sugar beet in the country, more than 50 % of sunflower and almost 100 % of grapes are obtained. The article analyzes the land resources of the Rostov region, considers the features of natural and agricultural zoning. Both positive and negative trends in the efficiency of the use of land resources of the region have been identified.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.181
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 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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