Trend of Growing Season Characteristics of Semi-Arid Arusha District in Tanzania
Bibliographic record
Abstract
The timing and distribution of rainfall determine both the length and quality of the growing season, and hence have important implications for agricultural production and food security. Using over 50 years of climatic data for Arusha District in Tanzania, the paper presents an analysis of growing season characteristics and other meteorological variables. Results indicate that the climate of Arusha District and Oljoro in particular is changing. Both the length of the growing season and number of wet days within the season are showing a decreasing trend as rains appear to be starting later than they used to in the past. Other meteorological variables such as temperature, wind speed and reference evapotranspiration are showing an increasing trend. Changes in land use/cover in and around the study area in the 1970s through 1990s due to expansion of agricultural land and population pressure would seem to have fuelled the observed changes in the growing season characteristics. This does not augur well for rain-fed agriculture and thus judicial use of scarce water resources including rainwater harvesting would seem to be a viable option for sustainable agricultural production.
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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 | 0.000 | 0.000 |
| 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".