Analysis of nutritional value, global trade standing, and production-price relationship of green lentils: evidence from Türkiye (Turkey)
Bibliographic record
Abstract
This study was designed to reveal the nutritional importance and global trade standing of green lentils, and to analyze the correlation between production and price. The data of the research was obtained from the Turkish Republic Ministry of Agriculture and Forestry, the Turkish Grain Board, the Turkish Statistical Institute (TURKSTAT), and the Food and Agriculture Organization of the United Nations (FAO). The production function was determined using annual time-series. Lentil prices were the independent variable, and the lentil production amount was the dependent variable. Canada ranked 1st in global lentil production with a share of 34.6%, while Australia ranked 1st in global lentil exports with a share of 35.4%. Since agricultural production is greatly affected by the lagged values of the prices, the Koyck model was used as the model in the study. Green lentil production is affected by 3-year prices at most backward. There was a very strong correlation (0.837) between the variables. This study is important in terms of drawing attention to the nutritional and commercial importance of lentils and revealing producers’ sensitivity to prices. In this study, green lentil from legumes was examined; however, studies which the entire legume family can also be conducted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".