NEW METHODOLOGICAL ASPECTS OF DIGITAL AGRICULTURAL MANAGEMENT BASED ON SPACE MONITORING DATA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".