Effect of Humic Acid Application on the Yield and Quality of Flue-Cured Tobacco
Bibliographic record
Abstract
The field experiment was conducted to investigate the effect of of humic acid amount (600 kg/hm2, 900 kg/hm2, 1200 kg/hm2) at different fertilization time (base fertilization, topdressing fertilization and both) on the potassium content and chemical quality of Yunyan 97. The results showed that humic acid can significantly increase the potassium content in tobacco leaves. The coordination of potassium content in upper, middle and lower leaves reached for 1.52%, 2.55%, 2.63% respectively in the treatment of humic acid (1200 kg/hm2) at base fertilization and topdressing fertilization (1/2 respectively), The content of nicotine and total nitrogen remained in appropriate range in all treatments. Both base and/or topdressing fertilization of humic acid in high level leaded to a more reasonable Potassium/Chloride ratio, and the coordination chemical quality was better.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".