Towards an Ontology-Driven System For Building and Farming Greenhouses
Bibliographic record
Abstract
Greenhouse systems are considered a part of sustainable agriculture, whose objective is food security and safety while taking into consideration the conservation of resources such as soil and water. To promote sustainable agriculture through greenhouses, it is important to develop an intelligent system that helps stakeholders in decision-making concerning the construction and management of greenhouses. This system must ensure farming activities and monitoring procedures. This work concentrates on the farming activities such as pest control, disease protection, crop cultivation, treatment, etc, and their representation in the system. Ontology is used as a technology to represent the structured information in terms of concepts and the establishment of semantic relations among them. While many existing ontologies focus on agriculture management, the greenhouse domain lack comprehensive coverage, particularly in the operational farming activities that are necessary to ensure the agriculture sustainability. Therefore, there is a need to develop a greenhouse ontology-based system that address the stakeholders’ inquiries related to greenhouse construction and essential farming activities for greenhouse management. This paper presents a synthesis analysis of the existing ontologies in the domain of agriculture and greenhouses as well as a novel modular ontology that covers the greenhouse farming module.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".