Worm castings-based growing media with biochar and arbuscular mycorrhizal fungi for producing organic tomato (Solanum lycopersicum L.) in greenhouse.
Bibliographic record
Abstract
Organic vegetable production has specific research and innovation requirements which are not shared by other parts of the food and farming sector. A pot experiment was conducted to investigate the interactive effects of few permitted organic inputs such as arbuscular mycorrhizal fungi, biochar, and different ratios of peat:worm casting on tomato (Solanum lycopersicum L.) growth, mycorrhizal dependency, biomass production, fruit yield, and soil respiration. The experimental design was a factorial arrangement based on completely randomized design with three replicates. Factors included worm casting at three levels (0, 15 and, 30% of the media volume), organic peat-based potting soil at three levels (70, 85, and 100% of the media volume), two Glomus intraradices treatments (inoculated at sowing or un-inoculated), and two biochar levels (10% of total weight of the media or unlamented). Results indicated that worm casting × peat combination significantly affected all measured traits except for the number of fruits in plant and mycorrhizal dependency. Mycorrhizal symbiosis had a significant effect only on shoot dry weight and mycorrhizal dependency. Moreover, biochar application significantly affected shoot dry weight, stem diameter and carbon mineralization. Among the different ratios of worm casting and peat in the growing media, 15% worm casting × 85% peat formed the most suitable medium condition for plants and 100% peat without worm casting was the least suitable. The highest fruit fresh weight (228.7 g/plant) was achieved in 15% worm casting × 85% peat and the lowest fruit fresh weight (175.1 g/plant) was achieved in 100 peat treatment.
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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) | 0.000 | 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".