Cultivation of Agaricus blazei ss. Heinemann using different soils as source of casing materials
Bibliographic record
Abstract
Commercial productivity of the Agaricus blazei mushroom is closely related to both the quality of the compost and the choice of soil to be used as a casing material. This study aims to evaluate Agaricus blazei's productivity using two compost formulations and three soils. The two compost formulations were (i) crushed sugarcane bagasse (Saccharum officinarum (L.)) and Coastcross hay (Cynidon dactylon (L.) Pers.), and (ii) crushed sugarcane bagasse (Saccharum officinarum (L.)) and corn husk (Zea mays L.); they were amended with wheat bran, lime, gypsum, superphosphate and urea. The casing materials were extracted from three soils classed as Rhodic Hapludox, Xanthic Hapludox, and Humic Haplaquox. The Rhodic Hapludox soil material was mixed with fragments of Eucalyptus charcoal in the proportion of 4:1. The compost was prepared during six weeks and thereafter heat treated during 48 h at the end of the composting period. The sugarcane bagasse:coast-hay compost was superior to the sugarcane bagasse: corn husk compost. The Rhodic Hapludox plus charcoal casing material showed to be a better casing material than either the Xanthic Hapludox and Humic Haplaquox soil materials. The choice of the soils where the casing material is taken is an important factor to the success of the Agaricus blazei mushroom cultivation.
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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 | 0.001 |
| 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".