Climate Change as a Threat to the Environment and Productivity: Case Study from Nabatieh Governorate, South Lebanon
Bibliographic record
Abstract
Lebanon is one of the countries in which the agricultural sector is suffering from climate change. This sector is vulnerable to disasters and risks that often lead to reduced agricultural productivity and degradation in natural resources. This study aims to assess the impact of climate change on agriculture, productivity and environment in order to identify the needed interventions in the livelihood zones in the Nabatieh Governorate. The assessment is done by direct interviews with 137 farmers selected from the study area based on a sampling plan. The main results showed that 40% of the farmers consider agriculture as a primary source of income, and the average percentage of cultivated area has decreased 25% between 2017 and 2021. Based on a statistical analysis using the XLSTAT program, a relationship can be identified between cultivated area and productivity between 2017 and 2021. It was observed that 85% of the samples experienced a rise in temperature ranges in the past 5 years, and were also affected by its consequences, such as increased pest proliferation and weed production in addition to its effects on crop yield and quality. Concerning the yield of crops in our study area, all yields decreased and their costs increased between 2017 and 2021.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".