Sulfur Uptake from Fertilizer Fortified with Sulfate and Elemental S in Three Contrasting Climatic Zones
Bibliographic record
Abstract
Field trials with labeled fertilizers can be used to provide information on fertilizer efficiency, residual value, and nutrient fate. We assessed the uptake from elemental S (ES) and sulfate S (SO4-S) in S-fortified monoammonium phosphate fertilizers by various crops at three sites in Argentina, Brazil, and Canada. The S sources were labeled with 34S, and the 34S abundance in the plant tissue was analyzed at an early stage and at maturity over two consecutive years. At the sites in Argentina and Canada, the recovery of ES in the crop was smaller than that of SO4-S in the first year, while the opposite was true in the second year. At the Brazilian site, the recovery of ES was similar to that of SO4-S in the first year, but higher in the second year. In the Argentina and Canada sites, the cumulative recovery of SO4-S was >65% and that of ES was 20−25%. In the Brazilian site, the cumulative recovery of SO4-S was 9% and that of ES 16%. The higher recovery of ES than of SO4-S in the Brazilian site was attributed to leaching of added SO4-S and relatively fast oxidation of ES due to the warm climate. These results suggest that ES may be more suitable than SO4-S as a fertilizer S source in warm humid climates, while inclusion of SO4-S in the fertilizer is recommended in colder climates as slow oxidation limits the initial availability of ES.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".