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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 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.004
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
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