∆ιάχυση-υιοθέτηση της γεωργίας ακριβείας : συγκριτική ανάλυση µεταξύ των περιφερειών της Ελλάδας
Bibliographic record
Abstract
In recent years, increasingly, rural development in each country performed new terms as it becomes recipient of the strong impact of a globalized environment. Furthermore, there is internationally a particularly significant increase in research interest as the degree of adoption and diffusion of precision agriculture and information technologies and communications (ICT) associated with it. It is reported that in the United States agricultural interest in precision agriculture reaches 88% presenting even very significant increase of 34% over the recent decade. Similarly large proportions reported for both Australia and Canada and the most European countries. Undoubtedly, these high ratios of interest farmers about the concept of precision agriculture in combination with the correspondingly high indicators ICT show that farmers really gain significant benefits and that their expectations is particularly large because the cost-benefit relationship of the adoption of innovative agricultural practices are very encouraging. This paper is an attempt geographical mapping, comparative presentation and analysis of the current state of adoption Precision agriculture in the various regions of the country, while investigated: (a) the main adoption of reasons, (b) the main reasons for not adopting, (c) the degree of familiarity with the concept of Precision agriculture and (d) the most favorable ways of agricultural education.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".