Drying and physico-chemical characterization of brewers spent grain for productivity improvement of Philippine smallholder animal production
Bibliographic record
Abstract
<abstract> <bold>Abstract.</bold> Brewers spent grain produced in Misamis Oriental, Philippines was disposed, transported, stored, and utilized in its wet form. Drying was rarely practiced. A solar energy- or biomass-powered batch dryer was designed and fabricated. Initial tests showed that it can reduce the moisture content from 71% to 10%, wet basis, in about 2 h, using a biomass furnace at an initial sample weight of 10 kg. Solar drying, however, took about 5.5 - 6 h and may not be practical to use under the present design. Basic physical attributes, proximate composition, and moisture sorption characteristics of the dried BSG were also assessed. Proposed measures to improve the performance of the prototype dryer and to encourage the use of drying and other spoilage prevention methods were also presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".