Climate Change Variability and Mitigating Measures by Rural Dwellers: the Perception of arable Farmers
Bibliographic record
Abstract
The study investigated the perception of arable crop farmers on climate change variability and the mitigating measures taken by them. It was carried out in Ahoada-East Local Government Area of Rivers State. Interview schedule was used to elicit information from the respondents. Proportionate sampling technique was employed to select ninety arable crop farmers from the study area. Data collected were analyzed using descriptive statistics and simple Ordinary Least Square (OLS) regression at 0.05 significant level was used to test the hypothesis. The findings from the study revealed that female dominated arable crop, and have been farming for the past 12 years. A higher percentage of the arable farmers were aware of climate change and were of the opinion that climate change was caused by bush burning, desertification, clearing of land for agriculture and act of gods. The effects of climate change on arable crops were poor/low yield, increased incidence of pest and diseases and induce spoilage of crops very fast. The mitigating strategies adopted by arable farmers to reduce the effects of climate change on their crops were, early harvesting of crops and mixed farming. Excessive rainfall and sunshine which were some of the signs of climate change affect arable crops when planted, which had led to low yield.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".