Estimation de la fertilité potentielle des ferralsols par la couleur
Bibliographic record
Abstract
A farmer-friendly method of determining the most suitable cultivation soils would help in transferring new integrated soil management technologies. The potential for using soil color (Munsell data) was tested by physico-chemical analysis of 1028 ferrallitic soil samples from 289 profiles unequally allocated above 7 deg N in Côte d’Ivoire. Soil hue variations in depth and along the toposequence revealed the existence of vertical and lateral gradients of soil hue. The relative contribution of the different descriptors (clay, sand, carbon, total nitrogen, total phosphorus, potassium, magnesium and calcium) to the three functions extracted using a discriminant analysis to differentiate the four groups of soils with different hues was evaluated as well as the analysis of variance to determine the possible groups number for each one of the descriptors. Differences between physico-chemical components of red (2.5YR and 5YR) and yellow (7.5YR and 10YR) soils were determined, especially for P, Mg and K in extension. A decreasing gradient of inherent soil fertility indicators with an increasing yellowness in soil hue was revealed using multiple regression models. The soils 2.5YR and 5YR were therefore deemed more appropriate for stable and sustainable agriculture.Key words: Hue, ferralsols, fertility, soil use
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".