How Well Do Critical Nitrogen Concentrations Work for Cabbage, Carrot, and Onion Crops?
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
With the introduction of nutrient management legislation in Ontario, there is a need to improve the efficiency of nitrogen (N) utilization. One possibility is to use critical nutrient concentrations in plant tissue as an indicator of the N nutritional status of the crop. Plant tissue analysis was used to determine the total N and nitrate-N (NO 3 -N) concentrations of cabbage ( Brassica oleracea var. capitata L. ) , carrots ( Daucus carota L.), and onions ( Allium cepa L.) grown in Ontario. The tissue samples were collected from plants as part of N fertilization studies from 1999 to 2001 on the organic soils in the Holland/Bradford Marsh area and the mineral soils near Simcoe, Ontario. Yield was assessed at harvest as an indicator of the N requirement of the crop. Testing the usefulness of critical NO 3 -N concentrations to indicate the N requirement of the crop was problematic because: 1) few published references were available to indicate a critical level of NO 3 -N in these crops; 2) tissue NO 3 -N concentrations were highly variable; and 3) field data rarely matched published references. Tissue total N concentrations from the trials corresponded to published critical N concentrations in some cases, however, the use of published critical N concentrations would have resulted in either over or under-application of fertilizer to the crops. Cultivar, soil type, and climate were shown to affect tissue N concentrations. Based on these results it was concluded that local research and field verification is required before tissue N critical nutrient concentrations become useful for determining fertilizer needs of cabbage, carrots, and onions grown in Ontario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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