Funções de pedotransferência para retenção de água e condutividade hidráulica em solo submetido a subsolagem
Bibliographic record
Abstract
This experiment aimed to develop pedotransfer functions, based on soil physical-hydric attributes, to estimate the water retention and hydraulic conductivity. The experiment was carried out in area with a Conilon (Coffea canephora Pierre) coffee crop irrigated and subjected to subsoiling in the row of planting. Undeformed samples were collected on the row (P1) and inter row (P2) of the crop from the layer 0 to 0.80 m, to determine the water retention curve in the soil, and soil conductivity by using Guelph permeameter. The pedotransfer functions showed underestimates for the potentials 0, -500 and -1500 kPa and -6, -10, -33 and -100 kPa, respectively in P1 and P2. In P2 the average relative error for the potential percentage was 8.42%, which is higher than the P1 (4.87%), resulting in higher error in the estimates of height of water stored in the soil. The water retention curve for the subsoiled soil may be estimated with error less than 5%, by clay, total porosity, macroporosity, microporosity and soil density attributes, and the hydraulic conductivity by soil clay fraction. For non-subsoiled soil, the retention curve may be estimated with error less than 9%, by the total pore volume and macro and micropores and the hydraulic conductivity by the coarse sand fraction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".