Estimation of Water Retention Curve for Soils of Bahia, Brazil, Based on Soil Particle-Size Analysis
Bibliographic record
Abstract
Soil water retention curve (SWRC) is an important soil attribute because it is a soil quality indicator and is fundamental to study water dynamics in the soil-plant-atmosphere system. Since the conventional SWRC determination is laborious and time-consuming, making it difficult to process a large volume of samples, pedotransfer functions have been used to estimate it by using other soil physical attributes easily determined. Thus, this study aimed to apply Arya-Paris model to SWRC estimation for soils of Bahia state, Brazil, based on soil particle-size analysis, and to compare estimated and determined data of SWRC. Samples were collected from horizons A and AB and/or B and/or C, for a total of 15 soils and 62 horizons. Particle-size was determined by automatic soil particle-size analyzer (PSA) based on g-ray attenuation and traditional Bouyoucos’ hydrometer (BH) method. Arya-Paris model showed better SWRC predictions for sandy soils, followed by clayey, loamy, and very clayey soils. Good model performance was observed for all soils together. The α 1 scaling factor provided better predictions, followed by α 3, and α 2 showed unsatisfactory behavior. BH method, using only 7 soil particle-size fractions, gave slightly higher predictions than PSA using 30 soil particle-size fractions.
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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) | 0.000 | 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 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".