The impact of sectors on agriculture based on artificial intelligence data: a case study on G7 countries and Turkiye
Bibliographic record
Abstract
The growing development of technology has had an impact on many sectors particularly business, communication, education and agriculture. In addition to its popularity, technology has brought many new concepts to the use of sectors, most of the important of which are cloud computing, artificial intelligence and cryptocurrencies. While the opportunities and concepts provided by technology have destroyed the existing job opportunities, they also introduced many positive opportunities like artificial intelligence, which can be considered as one of such positive innovations. The OECD artificial intelligence data of G7 countries and Turkey were used within the scope of this study. This study analyses the investment opportunities in agriculture and other sectors based on the artificial intelligence data. In addition to this study, both country-based and sectoral comparisons were made respectively. As a result, AI investments in the agricultural sector are generally at a lower level than other sectors. According to the analysis results, countries such as Türkiye and Canada are the countries that invest the most in the agricultural sector. This may reflect these countries' interest in agricultural potential and agricultural technology.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".