Factors Affecting Product Supply in the Domestic Agrarian Market in Azerbaijan
Bibliographic record
Abstract
The purpose of the study was to study, analyze, evaluate and forecast the factors affecting the formation of product supply in the domestic agrarian market. Socio-economic aspects of the factors influencing the growth of product supply in agrarian market in the country were considered for the purpose of the research. Statistical and econometric analysis was carried out by the method of generalization, grouping, systematic approach, the role and place of factors influencing the growth of product supply in agrarian market. The methodology used is based on econometric analysis of time series. The first step involves the formation of an order of integration of variables included in the model and utilizing several unit root tests such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. The cointegration approach is based on the ARDL model and the boundary test. The relative volatility of some indicators, which indirectly depends on oil prices, has led to some limitations in the study. The originality of the research is a complex statistical and econometric analysis of the factors influencing the increase of product supply in the domestic agricultural market, and the scientific novelty is the determination of the dependence of product supply in the agricultural sector on government support, including investment. The application of the article in the development of this section on the impact of agricultural supply on food security is of practical importance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".