A sample preparation method for reducing variability in the chemical analysis of mineral fertilizers
Bibliographic record
Abstract
Analyses of fertilizer are essential to ensuring that fertilizer sold to final users presents chemical and physical qualities in the range determined by law. As regards the sampling of fertilizers, the official method currently used in Brazil for sampling preparation is to reduce the size of the sample (from ~ 3 kg to ~ 0.25 kg) by quartering, followed by grinding (so as to pass through a 0.85 sieve) and nutrient quantification. Herein, we propose an alternative method of sampling preparation by grinding the total sample (~ 3 kg) before quartering to improve accuracy and reduce segregation during quartering. Six formulations of fertilizers (basal samples) were weighed (0.01 kg precision) and sampled according to the two methods (official and alternative), followed by the quantification of nutrient concentration in duplicate. Results showed that both methods presented similar nutrient concentrations for most formulations compared to the basal samples. However, the alternative method presented higher precision (less variation between replicates) and accuracy (versus the basal samples) than the official method. Consequently, the alternative method can be used for sampling preparation fertilizers with high accuracy and precision in determining nutrient concentration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".