The Potential Reflections of National Agricultural Research on the Solution of Global Agricultural Issues
Bibliographic record
Abstract
Global warming and water availability, loss of biodiversity, impacts of climate changes on agriculture, environmental pollution, food security and safety and renewable energy are worldwide problems affecting agricultural sustainability. In this respect, food safety, climate change, system management and risk management are becoming hot topics in agriculture. Increasing productivity and creating economies of scale, intensive technology and renewable energy use, establishing regional agro-industrial ecologies of life cycle assessment, networking marketing and trade and reducing risk are some proposed solutions. International organizations have been focusing on national and international research projects in order to determine global problems in agricultural sustainability and to diversify proposed solutions. Unfortunately, databases of research projects in the world are not connected for data, data mining, and big data processing purposes yet. Moreover, basic research, applied research, and experimental development do not complement each other. Projects are funded by national scientific and technological research councils. Due to the fact that project final reports and results are mainly published in native languages, such material cannot be utilized for the benefit of global issues. The objective of this study is to examine final reports of some national projects results of Turkey in order to understood well whether they are valuable to translate for international uses or not. We recommend that international organizations; such as OECD, FAO, etc. should collaborate on translating project final reports into English, which would be helpful in coping with global agricultural problems. Long-term surveys in agriculture should also be conducted by all nations to understand agricultural changes and technological development.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".