Establishment of Sufficiency Ranges to Determine the Nutritional Status of ‘Gigante’ Forage Cactus Pear—Macronutrients
Bibliographic record
Abstract
Determining the sufficiency range of essential macronutrients in plants is of utmost importance for successfully diagnosing the crop’s nutrient demands, thereby improving fertilizer recommendations. The aim of this study was to establish the macronutrients ranges in the cladodes for the evaluation of the nutritional status of ‘Gigante’ cactus pear. Macronutrients contents of cladodes and dry matter yield in 72 plots were used. The experiment consisted of four cattle manure rates (0, 30, 60, and 90 Mg ha-1 year-1), three spacings (1.00 × 0.50, 2.00 × 0.25, and 3.00 × 1.00 × 0.25 m) and two production cycles, arranged in a 4 × 3 × 2 factorial in randomized blocks, and three replicates. Sufficiency ranges of plots with dry matter ≥ 19.93 Mg ha-1 cycle-1 were determined as well as the mathematical chance for plots with dry matter ≥ 23.75 Mg ha-1 cycle-1 and the critical level. The sufficiency ranges, critical level and mathematical chance are, respectively, for each nutrient in g kg-1: N, 12.7-18.5; 14.4; 15.5-19.7; P, 1.0-1.8; 1.0; 0.4-1.7; K, 31.6-44.1; 31.9; 33.7-39.7; Ca, 23.2-32.8; 24.6; 25.0-29.6; Mg, 9.5-14.3; 10.2; 7.4-14.0; and S, 1.1-2.0; 1,1; 1.0-1.8. The mathematical chance model was adequate for all macronutrients except for P and Mg which showed low sufficiency range values. Sufficiency range and critical level of nutrients are useful for diagnosing macronutrient contents and improving the nutritional status of ‘Gigante’ forage cactus pear.
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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 | 0.000 |
| 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".