Effects of the herbicide hexazinone on nutrient cycling in a low‐pH blueberry soil
Bibliographic record
Abstract
The herbicide hexazinone was applied as the commercial formulation Velpar L at field-rate (FR) concentrations of FR (14.77 microg ai g(-1)), FRx5 (73.85 microg ai g(-1)), FRx10 (147.70 microg ai g(-1)), FRx50 (738.50 microg ai g(-1)), and FRx100 (1477.00 microg ai g(-1)) to acidic soil, pH 4.12, taken from a lowbush blueberry field. Hexazinone was tested for inhibitory effects on various transformations of the nitrogen cycle and soil respiration. Nitrogen fixation was unaffected by hexazinone levels up to FRx100 following a 4-week incubation period. Ammonification was initially inhibited by all levels of hexazinone, but after 4 weeks, ammonification in all treatment systems was equal to or greater than the control. Nitrification was more sensitive to hexazinone; however, application at a field-rate level caused no inhibition. Inhibitory effects were noted above FR after a 2-month endpoint analysis and above FRx5 after a 6-month endpoint analysis. Hexazinone concentrations up to and including FRx100 stimulated denitrification. Soil respiration was also stimulated over a 3-week period when applied at a level up to 100 times the recommended field rate. In general, it was found that when applied at the recommended field application rate, hexazinone does not adversely affect the nitrogen cycle or soil respiration in acidic lowbush blueberry soils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".