Small Farm Scale Production of Aerobic Compost from Hardwoods Predigested by<i>Lentinula edodes</i>
Bibliographic record
Abstract
The shiitake mushroom industry produces a waste product in the form of spent hardwood logs that have been partially digested by the lignicolous fungi, Lentinula edodes. Currently, there is no economically viable mechanism for disposal of these logs. The objectives of this research were to test several methods of composting chipped shiitake logs and to evaluate the use of the compost material for spinach (Spinacia oleracea L.) production. Four methods for producing compost from spent shiitake logs were compared: (a) in-vessel tumbler; (b) wire mesh cage; (c) passively aerated static pile; and (d) actively aerated static pile. Compost was produced from a mixture of 85% chipped spent shiitake logs and 15% grass clippings. In order to adjust C:N to 30:1. urea was added. Scale and production cost of the aerated static pile methods were superior to the other two methods. The resulting compost contained relatively high concentrations of Ca and low concentrations of P. Composting of chipped shiitake logs increased (he total relative abundance of fungi relative to bacteria. The ability of (he compost to supply N to growing crops was assessed by amending soils for production of spinach in the greenhouse. Compost additions increased the growth of spinach without increasing soil nitrate-nitrogen levels. These results indicate that spent shiitake logs can be converted into compost by methods appropriate for small farms and that the resulting compost can be used to stimulate crop growth.
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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".