Effect of enzyme addition on the nutritive value of six lupin cultivars with different alkaloid content
Bibliographic record
Abstract
An experiment was conducted to study the effect on chick performance of crude enzyme preparations when added to diets containing six different cultivars of lupin (Lupinus albus L.) seed grown in Canada. The lupin cultivars studied were Pnognus, NL, LAD, Hutterite, Amiga and Brandon. The total alkaloid content of these cultivars was 0.01, 0.02, 0.01, 0.04, 0.01 and 1.42%, respectively. The weight gain and feed consumption of the birds fed the high alkaloid content were reduced significantly (P < 0.0001; up to 43 and 31%, respectively) and the feed to gain ratio increased significantly (P < 0.0001; up to 21%) in comparison to the other cultivars. The addition of enzymes (0.1% each of Energex, Bio-Feed-Pro and Novozyme) increased significantly weight gain (P < 0.0033) and feed consumption (P<0.0116) by 5 and 1%, respectively, and reduced (P < 0.0055) feed to gain ratio by 4%. This was only seen in the low alkaloid cultivars. These results suggest that the enzyme addition improve the nutritional value of lupin cultivars with low content of alkaloid and that lupin alkaloids depress chick performance.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".