Soil Physical Quality Indicators and Refinement of the Evaluation Method through the Srelative
Bibliographic record
Abstract
This study aimed to verify the efficiency of indicators of measure of physical attributes’ alterations and to refine the Srelative determination method in order to increase its sensitivity to soil physical alterations. Soils under Ficus carica L. cultivation (with 0, 20, 40 and 60% of liquid bovine biofertilizer in the irrigation depth) and under forest were used. Parameters evaluated included soil granulometry, soil bulk and particle density, soil water retention curve (SWRC), porosity and the indices S and Srelative. The experimental design was completely randomized with four replicates. For Srelative refinement, with the SWRC containing only textural porosity, the soil was dispersed in water and with the addition of 1 N sodium hydroxide (with and without removal of sodium through washing). An ANOVA was performed for 0, 20, 40 and 60% of biofertilizer in 0-10, 10-20 and 20-30 layers; Dunnett test was used to compare the mean values of S-index and Srelative-index. With respect to four methods to obtain the Srelative-index the means were compared by Tukey test. Tests of line parallelism and intercept were performed for the regressions between each of the soil physical variables and Srelative-index obtained. It was found that S and Srelative indices were sensitive to soil physical alterations caused by the application of the biofertilizer; Srelative-index was sensitive to variation in soil bulk density and total porosity and the Srelative-index obtained from the method of soil dispersion in water is more sensitive to soil physical alterations in comparison to Srelative-index obtained through ADFE.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".