Hardware and Software Complex for Monitoring Soil and Climatic Parameters
Bibliographic record
Abstract
The article discusses agriculture digitalization problem in Russia. Agribusiness development dynamics in Russia and Western Europe countries, USA and Canada are compared. Russian regions positive experience in digitalization tools implementation is presented. The article proposes one of the possible tools for agriculture digitalization in Russia - hardware and software complex for monitoring soil and climatic parameters. Such system expediency, its benefits for science and agriculture are given. Main principles, technologies and algorithms used in system development are described, as well as main development problems and ways to solve them. The article describes in detail hardware and software complex structure and its elements - probe, base station and web interface. The system, each element structure, its composition and purpose, block diagram and operation algorithms description are also described in detail. The article also presents and describes software and hardware complex elements constituent part - sensors, transmitters, a microcontroller, etc. A separate block in article is software and hardware complex testing stage. This block describes testing methodology and its implementation main stages. A temperature sensors comparison inside device was carried out and climatic parameters comparison obtained by software and hardware complex and using RP5 weather forecast service.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".