Critical Tissue Identification and Soil–Plant Nutrient Relationships in Dicer Carrot
Bibliographic record
Abstract
Fertility management has been a major concern for carrot growers because there has been little or no yield response to fertilizer application in various trials, even when fertilizer is based on soil test recommendations. Tissue testing may be an appropriate method to manage fertilizer applications to carrots. A greenhouse trial was conducted to identify critical tissue(s) at various growth stages that correlate with yield, establish relationships between nutrient concentrations of critical tissue(s) and nutrient concentrations in soil, and establish relationships among nutrient concentrations of critical tissue(s), nutrient concentrations in soil, and yield. Critical tissues varied for each nutrient studied at each growth stage. Correlations revealed significant relationships between nutrient concentrations of critical tissues and soil largely at active bulking but very few at initiation of bulking. Trend graphs revealed tissue zinc (Zn) concentration had the strongest relationship with yield. There was a significant difference in root fresh weight (RFW) with nitrogen (N) at 100 µg/g, significantly higher than at 0, 300, 350, and 400 µg/g. Greenhouse results suggest fertilizer with an N equivalent of 100 µg/g optimized yields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".