Agricultural Researchers’ Awareness of the Causes and Effects of Climate Change in Edo State, Nigeria
Bibliographic record
Abstract
The study assessed Agricultural researchers’ awareness of the causes and effects of climate change in Edo State, Nigeria. Data for the study were collected from 112 respondents and were analyzed using descriptive and inferential statistics. Findings indicated that 45.5% of the respondents were between the ages of 31-50 years with 64.2% having a work experience of 5-10 years. Results revealed that 96.4% and 94.6% of the respondents were aware of gas flaring and oil spillage as causes of climate change. Also, 98.2% and 95.5% of the respondents were aware of increase in temperature and change in rainfall pattern respectively as some of the effects of climate change. There was a significant relationship between information sources and awareness of causes of climate change (r = 0.32; p < 0.05). It was recommended that agricultural researcher should be pragmatic and proactive in the pursuit of issues relating to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".