Water Table Control for Increasing Yield and Saving Water in Cranberry Production
Bibliographic record
Abstract
Water table control has been successfully tested to improve the sustainability of water management in cranberry production. In the province of Québec (Canada), three sites were investigated to determine the optimum water table depth below soil surface (WTD) using three criteria: (1) increasing yield without decreasing fruit quality; (2) minimizing the amount of water needed by the sprinkler system; and (3) avoiding hypoxic stresses in the rhizosphere. Our results show that the final yield, the berry sugar content, the total number of berries, the number of berries per upright, and the fruit set were maximized when the WTD was 60 cm. Sprinkler water savings of 77% were obtained where the WTD was shallower than 66 cm. In order to avoid hypoxic conditions due to poor drainage, the water level in the canals surrounding the beds should be lowered to 80 cm when a rainfall or a frost protection irrigation is anticipated. All sides of a block of beds must be surrounded by canals to ensure a uniform WTD and to avoid lateral hydraulic gradients that could cause peripheral seepage losses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 teacher head, 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".