Chemical Extractors to Assess Potassium Availability in Glauconitic Siltstone
Bibliographic record
Abstract
This paper aimed to evaluate the efficiency of chemical extractors to measure the availability of potassium (K) in glauconitic siltstone. An experiment with successive crops of beans and maize was installed under field conditions in Quirinópolis, Goiás State, Brazil, in a typical ortic Neossolo Quartzarênico. For both crops, the experimental design was a randomized block with four replications, resulting in a total of 24 experimental plots. Two sources and four doses of K2O were used, in addition to a control treatment with any K fertilization. The sources used were glauconitic siltstone and potassium chloride (KCl), and the doses applied through glauconitic siltstone corresponded to 1, 2, 4 and 8 times the dose of K2O applied via KCl as a reference. The following extractors were used: neutral ammonium citrate (NAC), citric acid 2% (CA), CA + ammonium fluoride 0.5% (NH4F), tartaric acid 5% (TA) + sodium fluoride 0.5% (NaF) (1:100), TA + NaF 0.5% (1:500), hydrochloric acid (HCl), and the method for extracting potassium from silicatic materials (MAPA-8.2.4.2). Among the tested extractors, the best one regarding productivity was the MAPA-8.2.4.2. The NAC and CA extractors showed the lowest correlations and should not be used henceforth to quantify soluble K from glauconitic siltstone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".