Modelling the Soil Water Balance of a Sugarcane Crop in Sindh, Pakistan with SWAP93
Bibliographic record
Abstract
The soil water balance was simulated for a sugarcane (Saccharum officinarum L.) crop grown at a site in Sindh, southeast Pakistan. Data from drainage lysimeters with a water table depth (WTD) set at 1.5 m or 2.25 m from the soil surface were used to calibrate the SWAP93 model. Surface irrigation, subirrigation and drainage were monitored in the lysimeters, and climatic data were collected at the site for day of the year (DOY) 46 to 289. SWAP93’s ability to predict soil water balance components such as net bottom fluxes below the plant root zone (i.e., drainage or capillary rise) and evapotranspiration was evaluated. Daily and cumulative drainage were overestimated for both WTDs over the season. However, if the simulation was stopped before the heavy rainfall of DOY 201–205 (approx. 200 mm), daily drainage for the 2.25 m WTD lysimeters and cumulative drainage for both WTDs were underestimated. This rainfall showed no effect on evapotranspiration estimates. Weekly and cumulative ET were both underestimated, suggesting that the ET computational method needed to be revised for the study region.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".