390 Creating kibbles with unique starch, fiber, and protein profiles using Canadian ingredients.
Bibliographic record
Abstract
Within Canada, agronomic conditions determine cultivated crops. In Eastern Canada, crops are mostly corn and soybean. In Western Canada, crop rotations exists with wheat, barley, and oats as cereal grains, canola, and pulse grains such as field pea and lentil. Seeds of these crops together with their co-products may serve as plant-based ingredients to provide starch, fiber, protein, and fat for kibble production. From coast-to-coast, products from raised fish, poultry and livestock may serve as animal-based ingredients to provide protein and fat. While animal and plant protein differ in AA profile and digestibility, changes in functionality beyond gluten content are limited. In contrast, fatty acid profiles differ in omega 3 to 6 ratio among plant and animal sources. Analyses of fiber and starch have focused on assessing quantity; however, both have a range of functional properties. Fibers ranging from low to high viscous affect digesta flow and from slowly to rapidly fermentable alter production of volatile fatty acid (VFA) serving as energy for the gut or whole body. Hulls of cereal grains and pulse grains are low viscous and slowly fermentable. In contrast, fibers such as β-glucan in barley and oats and oligosaccharides in pulse grains are rapidly fermentable and may serve as prebiotic. Cereal grains tend to have a faster rate and total extent of starch digestion and thus glycemic index than pulse grains due to more amylose in pulse grains. Consequently, pulse grains may provide less digestible starch than cereal grains and more resistant starch that is fermented, thereby changing starch from glucose source into VFA source. Resistant starch thus basically acts as dietary fiber. However, differences in starch digestion among grains are reduced with extrusion of kibbles. Functional characteristics of fiber and starch can be altered using ingredient selection and should be considered in petfood formulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".