Particle Size Imbalance Index from Compositional Analysis to Evaluate Cereal Sustainability for Arid Soils in Eastern Algeria
Bibliographic record
Abstract
For homogeneous fertilization and crop management practices, this work hypothesized that texture could influence cereal yield, particularly in dry regions. Particle size analysis could help improve knowledge of the soil-plant relationship to obtain favorable conditions for better yield. The objective of this work is to develop a single granulometric index for durum wheat (Triticum durum) that is well correlated with yield. For this purpose, 350 independent samples of cereal soils from eastern Algeria were taken and the recorded yields were linked to these samples. The cutoff yield, which separates sub-populations with acceptable yield from those with less acceptable yield, was determined from the inflection point of the cumulative variance ratio functions related to yield by the Richards’ equation. The result obtained is 2.0 Mg.ha−1, with a theoretical critical chi-square value of 4.2, close to 4.6, which is the critical value of r2granulo as obtained by the Cate-Nelson procedure. The five-granulometric indices were found to be symmetrical around zero as follows: ±0.83 for clay (IC), ±1.73 for fine silt (IFL), ±0.31 for coarse silt (ICL), ±0.44 for fine sand (IFS), and ±1.30 for coarse sand (ICS). The two fractions that most influence the textural imbalance are fine silt (IFL) and coarse sand (ICS), with a contribution of 41% and 37%, respectively. The critical single imbalance index r g r a n u l o 2 can be used for determining cereal suitability for soils in the arid region of eastern Algeria. The lower the r g r a n u l o 2 is, the better the soil for cereal crops.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".