Optimal Irrigation for Onion and Celery Production and Spinach Seed Germination in Histosols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing water scarcity in humid regions requires that food production increase its water use efficiency. Because the hydraulic characteristics of Histosols are different from those of mineral soils, water management for vegetable production must be adapted accordingly. The objective of this research was to determine the optimal soil water potential for irrigating onion (Allium cepa L.), celery ( Apium graveolens L.), and spinach ( Spinacia oleracea L.) crops in muck soils. Onion and celery were subjected to three irrigation treatments scheduled when tensiometer readings reached –10 or –20 kPa for onion and –30 or –50 kPa (2008) and –15 or –30 kPa (2009) for celery compared with drier control treatments for both crops. For spinach, two irrigation treatments (–10 and –20 kPa) and a control (drier) were tested. Optimal onion marketable yields and jumbo size were obtained from irrigation at potentials above –20 kPa at the bulbing stage. Celery had the best yields with the treatments of 2009 relative to the drier control. The highest spinach germination rate and yield were obtained at –10 kPa. Reliable estimates of the optimal thresholds were consistent with calculations performed using a simple analytical solution to Richards’ equation and soil characteristics. Irrigation thresholds for matric potential in a muck soil were shown to be crop specific and could be derived from a model and basic soil hydraulic characteristics.
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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 | 0.001 | 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 it