Mineralization and nutrient release pattern of vermicast-sawdust mixed media with or without addition of Trichoderma viride
Bibliographic record
Abstract
A combination of vermicast and sawdust mixed medium is commonly used in horticulture, but the added benefit of microbial inoculation and mechanism of nutrient availability are unknown. This study was done to determine nutrient mineralization and nutrient release patterns of different combinations or a mix of vermicast-sawdust growing media amended with or without Trichoderma viride (105 spores/g). The mixed-media treatments were (1) 80% vermicast+20% sawdust; (2) 60% vermicast+40% sawdust; (3) 40% vermicast+60% sawdust; (4) 20% vermicast+80% sawdust; and (5) sawdust alone (control). Total dissolved solids, electric conductivity and salinity increased with each sampling time following submergence in deionized. Nutrients released from media without T. viride were significantly higher than the corresponding media with added T. viride. Overall, the starting total nitrogen of the different media did not change during the incubation period, but nitrate-nitrogen was reduced to a negligible amount by the end of day 30 of incubation. A repeated measures analysis showed a significant effect of Time*T. viride*Treatment on total dissolved solids. Redundancy analysis demonstrated a positive and strong association between media composed of ≥40% vermicast and ≤60% sawdust with or without T. viride and mineral nutrients released, electrical conductivity, total dissolved solids and salinity. These findings suggest that fast-growing plants may benefit from 40% to 60% vermicast added to 40% to 60% sawdust without T. viride while slow-growing plants can benefit from the same mixed medium combined with the addition of T. viride. Further investigation is underway to assess microbial dynamics in the mixed media and their influence on plant growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".