Indigenous Weather Forecasting Systems: A Case Study of the Abiotic Weather Forecasting Indicators for Wards 12 and 13 in Mberengwa District Zimbabwe
Bibliographic record
Abstract
Residents of wards 12 and 13 in Mberengwa depend on agriculture as a source of livelihood. They have since developed their own indigenous weather forecasting systems which they have been using in conjunction with meteorological weather forecasts in the planning and execution of their agricultural activities. These systems are used singularly or are complimented with the conventional meteorological forecast from the Meteorological Services Department. The main objective of this research was to identify the abiotic weather forecasting indicators as well as to acquire information on how they are used. Questionnaires, focus group discussions and interviews with key informants were used to collect the data. Key informants were those whose time of residence in the wards was more than 50 years. Investigations revealed that the residents rely mostly on environmental indicators for planning agricultural activities. Results showed that of the two categories of indigenous abiotic weather forecasting indicators, weather forecasts derived from weather indicators were the mostly used followed by forecasts derived from celestial indicators. There were however differences on the interpretations of the behavioural signs or indicators which were used in the production of the forecasts from these abiotic factors.The study recommends that further research should be carried out on the application and on the statistical evaluation of the precision of the indigenous forecasts. Attempts should also be made to document the abiotic weather indicators and the behavioural signs from which these forecasts are derived. The establishment of an effective indigenous weather forecasting system as part of a decision support system would go a long way in improving food security for this area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".