Pipette Method: Errors Resulting From Aliquot Collection Depth in Soil Clay Quantification
Bibliographic record
Abstract
Granulometry represents the relative proportions of the fractions that compose the soil, being an important agronomic tool to infer mean values of density, water availability and cation exchange capacity, besides being useful in soil classification. Among the methods employed to determine the fractions composing the soil, those which consider the separation by sedimentation for the clay fraction still have problems in the analytical protocol, which are directly responsible of errors in the results obtained. Given the above, this study aimed to evaluate the best pipette immersion depth to collect the aliquot containing only clay, to calculate and discuss the errors associated with collection of the aliquot containing clay fraction in soil granulometric analysis. Samples for granulometric analysis were collected in the superficial layer and top of the B horizon of an Argissolo Amarelo, corresponding to the textural classes sandy loam and sandy clay. Regardless of soil textural class, the depth h = 5 cm established in the calculation using the Stokes’s equation leads to overestimation and underestimation of clay and silt fractions in the soil. The collection should be performed with the pipette tip positioned at h/2 = 2.5 cm.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".