Towards an Ontology-Driven System For Building and Farming Greenhouses
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it