Thermal decomposition, chemical composition,<i>in vitro</i>digestibility and gas production and<i>in situ</i>degradability of oilseed residues from the biofuel industry
Bibliographic record
Abstract
Thermal analysis could rapidly and easily predict nutritional value of ruminant feeds. The hypothesis is that crambe meal (CM) has a quality similar to that of soybean meal (SM), and the objective of this study was to determine the nutritional characteristics of CM and compare them to those of SM. CM had greater concentrations of phytic acid (26.3 vs. 16.0 g/kg) and phenol compounds (615 vs. 393 mg gallic acid (GAE)/kg) than SM. In vitro dry matter (DM) digestibility was lower for CM than SM (752 vs. 975 g/kg DM). Cumulative in vitro gas production at 48 h of incubation (14.1 vs. 19.4 mL/100 mg substrate DM), and energy release (4.5 vs. 5.7 kJ/g substrate DM) were lower for CM than SM. CM had a higher concentration of low digestible fiber, hence degradability of DM was lower and the proportion of indigestible fraction was greater for CM than SM. High concentrations of indigestible compounds were likely responsible for lower gas production of CM compared to SM. These results suggest CM quality is lower than that of SM and that thermal analysis is a useful tool to precisely determine the nutritive value of oilseed residues.
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 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) | 0.000 | 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 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".