Statistical Evaluation of Seasonal Effects to Income, Sales and Work- Ocupation of Farmers, the Apples Case in Prizren and Korça Regions
Bibliographic record
Abstract
Abstract This paper is focused on the statistical assessment of seasonal effects on farmers' income, their workocupation in farm, and sales of apple products. In focus of this study we have taken two regions Prizren and Korça. By making a comparing between Albania and Kosovo, with regard to significance of the model of seasonal effects for apples. In this paper we have used several statistical and econometric methods to evaluate the seasonal effects on economic phenomena taken in the study. We have used the variation indicators to show the distribution of the observed phenomenon. We also have used dummy variable models. Dummy variables are often used in time series analysis, in seasonal and qualitative analysis of applied data. Each dummy variable is set to 1 if the point of datas is received from a specified season and otherwise 0. To evaluate the seasonal effects in a time series through 1dummy variables, we need to use four dummy variables, one for each quarter, or three dummy variables and a constant. These variables use them as inputs or factors in a regression model. In our paper, we have categorized sales, for apples in 5 different periods. To estimate the magnitude of seasonal effects and to test their significansy are used four dami variables. The purpose of this paper is to show whether the pattern of seasonal effects for apples is significant.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".