Prioritising the Replanting Schedule of Seedling Tea Fields on Tea Estates for Drought Susceptibility Measured by the SWAPDT Method in the Absence of Historical In-filling Records
Bibliographic record
Abstract
Due to the unpredictable natural droughts that occur, causing tea farmers significant losses in tea estates, a two-day method for distinguishing between drought tolerant (DT) and drought susceptible (DS) Camellia sinensis cultivars was developed. This work was based on known cultivars developed at the Tea Research Institute in Kenya and the Tea Research Foundation for Central Africa in Malawi. This paper contains an in-depth description of the application of the SWAPDT method on four 60-year old, C. sinensis seedling fields in Kenya. The in-filling history of the four fields due to drought-related deaths was obtained from historical records. The SWAPDT method scores correlated very well with the historical records. It has been indicated, from the results obtained in this study, that a sample size of 20 tea trees is sufficient to accurately determine the drought susceptibility of a large tea field of approximately 5-20 hectares, containing 50 000-200 000 tea trees, were the difference between their mean values, as measured by the SWAPDT method, is approximately 10%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".