Assessment of Rootzone Water Redistribution in Corn Following Irrigation
Bibliographic record
Abstract
An understanding of the water redistribution pattern within the corn rootzone will help design better irrigation and drainage systems. The objective of this research was to use time-domain reflectometry (TDR) miniprobes to measure water content within the rootzone of corn at different locations within the rootzone as a function of time. Forty-five TDR miniprobes were installed, in three vertical planes, for measuring volumetric water content and salinity within the rootzone of a corn field located in Winkler, Manitoba. The probes were inserted at 0.1, 0.2, 0.4, 0.6, and 0.8 m depths from the ground surface and at 0.12, 0.24, and 0.36 m radial distances from the base of the corn plant. The soil was thoroughly wetted by applying 50 mm of water within the experimental site. The volumetric water content was measured before and at periodic intervals after the irrigation event. The evening following the irrigation event showed an increase in water content within the entire soil profile. The water content started to decline as the days progressed. However, during the mornings, the soil within the root zone seemed to show an increase in water content when compared to the previous afternoon. An examination of the water content distribution within the root zone indicated an upward migration of water from soil layers located below 0.8 m.
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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 | 0.000 |
| 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) | 0.000 | 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".