Water table level management as an irrigation strategy for cranberry (<i>Vaccinium macrocarpon</i>Aiton)<sup>1</sup>
Bibliographic record
Abstract
Cranberry production is a water intensive practice that requires irrigation during summer months to achieve maximum yields. Previous studies have found that root zone tension maintained between −4 and −7.5 kPa allows for maximum yields without over irrigating. The present study looks at the effects of managing a water table to supplement overhead sprinkler irrigation with upward flow. Two drainage systems, controlled and free, were implemented in a cranberry bed constructed of fine sand. The controlled drainage system used existing drain tiles and a sump to maintain an artificial water table, while the free drainage system had no manipulation of the water table. Daily upward flow and water table level were measured in four locations, across the length of the bed, for each drainage system. Comparing upward flow with evapotranspiration (ET) rates, approximately 30% of maximum daily ET can be met by holding a water table between 500 and 600 mm. Numerical simulations indicate that water tables shallower than 500 mm allow for nearly full supply of ET, but at root zone soil water tensions too wet for the best productivity. Field results and model simulations indicate that water table management can be a useful tool in cranberry irrigation.
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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".