Bibliographic record
Abstract
Research and observations indicate that increases in atmospheric carbon dioxide (CO2) are raising global and regional temperatures, and producing changes in other climate variables that drive the terrestrial hydrological cycle, most notably precipitation and potential evaporation. This paper presents results of a study conducted to evaluate the possible impacts of climate change due to doubling of atmospheric carbon dioxide on the reliability of Mazowe reservoir in Zimbabwe. The reservoir supplies most of its water to citrus plantations. Thirty years (1961-1990) of hydrological data (reservoir inflows) and meteorological data were collected from the Zimbabwe National Water Authority (ZINWA) and Department of Meteorological Services, respectively. outputs from the Canadian Climate Centre (CCC) model for the 2CO2 temperature and rainfall scenarios were used in the study. The Penman model was used to estimate potential evapotranspiration, while reservoir catchment runoff was simulated using the Pitman lumped conceptual model. Research findings revealed that doubling of CO2 in 2050 would significantly increase mean monthly temperature by 3°C, potential evapotranspiration (11.8%), rainfall (15%), runoff (23.5%) and annual reservoir yield (20.4%) at the 10% risk level. Based on the research findings, appropriate mitigation measures should be employed to minimise high rates of evaporation from the reservoir. on the other hand, the predicted high reservoir yield requires an increase in water use activities such as extension of irrigated area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".