Potential for use of Spent Substrate of Pleurotus Mushrooms Grown on Urban Waste as Feed for Dairy Cattle
Bibliographic record
Abstract
Mushroom wastes are available in high volumes, with 5 million tons of spent mushroom substrate (SMS) being disposed of globally every year. Due to this high availability, various forms of SMS have been researched for their use as alternative animal feeds. Additionally, experimental techniques can be used to grow certain mushroom species, such as oyster mushrooms (Pleurotus sp.) on various lignocellulosic waste materials. Therefore, the SMS from Pleurotus sp. grown on these waste materials may offer a promising conversion from a waste material to a low-cost, nutritionally sufficient feed. However, little research has been done to determine if feeds from Pleurotus SMS specifically grown on urban waste substrates offer the same benefits. Given rising awareness on circularity and urban self-sufficiency, growing mushrooms on urban waste is a promising solution which should be investigated. This paper assesses the feasibility of using SMS from golden oyster mushrooms (Pleurotus citrinopileatus) grown on urban waste as dairy cattle feed, comparing substrate ratios to determine which would result in the most desirable protein and fiber contents. SMS from three experimental substrates of cardboard and spent coffee grounds (SCG) were compared to traditional dairy cattle feeds. Treatments 2 and 3 were found to be suitable for use as additives to traditional feeds in small replacement amounts. However, both treatments also had high fiber content, which may affect practicality of use as feeds.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".