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Record W4408651941 · doi:10.34925/eip.2024.174.1.031

NEW METHODOLOGICAL ASPECTS OF DIGITAL AGRICULTURAL MANAGEMENT BASED ON SPACE MONITORING DATA

2025· article· ru· W4408651941 on OpenAlexaff
K.R. MADRAKHIMOV

Bibliographic record

VenueЭкономика и предпринимательство · 2025
Typearticle
Languageru
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsNordic Life Science Pipeline (Canada)
Fundersnot available
KeywordsAgricultureSpace (punctuation)Computer scienceData scienceEnvironmental resource managementEnvironmental scienceRemote sensingGeographyArchaeology

Abstract

fetched live from OpenAlex

In this article, the use of modern innovations in the field of Agriculture in management, in particular, the organization of a management system based on the data of space technologies. The results of space monitoring carried out in the Republic of Uzbekistan on the calculation of agricultural land areas and their clear boundaries, inventory of agricultural land and identification of real cultivated areas are also presented. В этой статье рассматривается использование современных инноваций в области сельского хозяйства в управлении, в частности, организация системы управления на основе данных космических технологий. Также представлены результаты космического мониторинга, проведенного в Республике Узбекистан, по расчету площадей сельскохозяйственных земель и их четким границам, инвентаризации сельскохозяйственных земель и определению реально обрабатываемых площадей.

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)0.012
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.010
Science and technology studies0.0010.006
Scholarly communication0.0100.007
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.177
GPT teacher head0.388
Teacher spread0.211 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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