Formalin fumigation and steaming of various composts differentially influence the nutrient release, growth and yield of muskmelon (Cucumis melo L.)
Bibliographic record
Abstract
Abstract Nutrient disorder and presence of disease-causing agents in soilless media negatively influence the growth of muskmelon. To combat these issues, use of environmentally-friendly sanitation techniques is crucial for increased crop productivity. The study was conducted under greenhouse and field conditions to investigate the effect of two different sanitation techniques: steaming and formalin fumigation on various media’s characteristics and their impact on muskmelon yield. Media: jantar, guar, wheat straw and rice hull and peat moss of 10% air-filled porosity and sanitized with formalin and steaming. Steaming of guar, jantar, and wheat straw increased the phosphorus (P) and potassium (K) concentrations by 13.80–14.86% and 6.22–8.45% over formalin fumigation. Likewise, P and K concentrations in muskmelon were higher under steaming. Steaming significantly inhibited the survival ofFusariumwilt sp.melonis, root knot nematode sp.meloidogyneand nitrifying bacteria in media than formalin fumigation. In conclusion, steaming decreased the prevalence of nitrifying bacteria and pathogens which thus improved the NO3−–N:NH4+–N ratios, P and K nutritional balance both in the media and muskmelon transplants. Hence, steaming as an environment-friendly approach is recommended for soilless media. Further, optimization of steaming for various composts with different crops needs to be investigated with steaming teachnique.
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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".