Fractionation of wheat distillers' dried grains and solubles by particle size and density improves its digestible nutrient content for rainbow trout
Bibliographic record
Abstract
Reveco, F. E. and Drew, M. D. 2012. Fractionation of wheat distillers' grains and solubles by particle size and density improves its digestible nutrient content in rainbow trout. Can. J. Anim. Sci. 92: 197–205. The nutritional value of wheat distillers' dried grains and solubles (WDDGS) in aquaculture is poor because of its relatively low crude protein (CP) and high fibre content. In this study WDDGS was fractionated using grinding, sieving and elutriation sequentially. The WDDGS was ground in a hammer mill using a 3-mm screen and sieved using six sieves (20M, 30T, 40T, 50T, 60M and 80M) into seven fractions (>841, 590–840, 426–589, 298–425, 251–297, 178–250 and<177 µm). Elutriation was then performed to further fractionate based on particle shape and density. The higher density sub-fractions from the three smallest particle size fractions were mixed to produce a fractionated WDDGS containing 20.7 MJ kg−1 gross energy (GE), 454.6 g kg−1 CP, 260.4 g kg−1 neutral detergent fibre (NDF) and 93.2 g kg−1 acid detergent fibre (ADF). The digestibility of the unprocessed and fractionated WWDGS products was assessed in rainbow trout. Apparent digestibility coefficient (ADC) of DM, GE, acid ether extract (AEE), ash and amino acids (AA) did not differ between the unprocessed material and the fractionated WDDGS (P>0.05). However, the ADC of CP was higher for fractionated WDDGS (0.88) than the unprocessed WDDGS (0.85) (P<0.05). This fractionation scheme can be used to improve the nutritional value of WDDGS for rainbow trout.
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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".