Comparative performance of scientific and indigenous knowledge on seasonal climate forecasts: A case study of Lupane, semi- arid Zimbabwe
Bibliographic record
Abstract
Seasonal climate forecasting (SCF) is weather prediction over a period ranging from 3-6 months period. Forecasting can be done using scientific forecasts (SF) or indigenous knowledge forecasts (IKF) systems. Forecast results can be very fruitful to smallholder farmers in semi-arid areas where rainfall is highly variable. Effective use of SCF has faced challenges including: rainfall variability, access to forecast information, interpretation of forecast results and generation gap. There is limited research on comparative performance of the two forecasts. The research seeks to evaluate comparative performance of the two forecasting methods in predicting outcome of the following rainfall season. The study was carried out in Daluka and Menyezwa wards of Lupane district, south-western Zimbabwe, which receives annual average rainfall of 450-650 mm. Focus group discussions and personal interviews were used in 2008/09 and 2009/10 seasons to capture farmers’ experiences and knowledge on SCFs and their application. The predicted outcome of the IKF and SF were compared with actual rainfall recorded from the predicted period. Results indicated high dependence on the use of the IKFs by Lupane farmers in predicting the outcome of the following season’s rainfall. Both the IKF and SF predicted inadequate rainfall in the two consecutive seasons and the results concurred with recorded rainfall in Daluka ward in the two seasons and in Menyezwa ward in 2009/10 season only. Results demonstrate that in the absence of SF, farmers may use IKFs. It is imperative that the two forecasts complement each other to increase farmer adaptation to climate variability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".