Nutrient Diagnosis Norms for Date Palm (Phoenix dactylifera L.) in Tunisian Oases
Bibliographic record
Abstract
Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm−1 and the global nutrient imbalance index (CNDr2) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for IN, (−0.44, +0.44) for IP, (−0.63, +0.63) for IK, (−0.94, +0.94) for ICa, (−1.05, +1.05) for IMg, (−0.80, +0.80) for IFe, (−0.74, +0.74) for ICu, (−0.80, +0.80) for IB, (−0.93, +0.93) for IZn, (−1.04, +1.04) for IMn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".