Single Screw Extrusion Processing of Soy White Flakes Based Catla Feed
Bibliographic record
Abstract
An initial investigation into the inclusion of soy white flakes (SWF) and high protein distillers dried grains (HP-DDG) in catla (Catla catla) diet, belonging to the family Cyprinidae, was conducted using a single screw extruder. Three isocaloric (302 kcal/100 g) ingredient blends containing graded levels of SWF in combination with HP-DDG and other required ingredients were formulated to contain a net protein content of 31.5% (wet basis). Extrusion processing was then performed using three levels each of SWF content, moisture content, and temperature gradient keeping a constant screw speed and die diameter. Effects of these variables on extrudate physical properties including: color, pellet durability index, bulk density, water absorption index, water solubility index, unit density and expansion ratio were extensively analyzed. Increasing the level of SWF resulted in increase in water absorption index and unit density but decrease in expansion ratio. The interaction effect of SWF content, moisture content and temperature were significant for color, pellet durability index, bulk density and expansion ratio. All the extrudates showed relatively high pellet durability and inclusion of SWF produced less expanded and more compact textured extrudates.
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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".