Effects of partial removal of alkaloids in sweet white lupin seed on performance and nitrogen balance in lambs
Bibliographic record
Abstract
The impact on animal performance of extracting alkaloids in lupin seed (LS) was evaluated with 30 lambs (average weight, 14 kg) fed for 90 d. Sunflower seed (SFS) in the control diet was replaced by intact LS, or LS steeped in water (detoxified) to remove alkaloids. The test diets contained either 15% (LUI-15) or 30% (LUI-30) intact LS, or 15% (LUD-15) or 30% (LUD-30) detoxified LS. Lambs fed SFS exhibited lower (P < 0.05) organic matter intake (OMI) and daily gains (ADG) than those fed LS. During the first 30 d of feeding, OMI was greater (P < 0.01) with diets containing LUD-15 or LUD-30 (752.7 and 727.9 g d–1, respectively) than with LUI-15 or LUI-30 (708.1 and 600.5 g d–1, respectively); ADG was also greater with LUD-15 and LUD-30 (P < 0.01) than with LUI-15 and LUI-30. Nitrogen retention ranged from 6.1 g d–1 for control to 14.6 g d–1 for LUD-15; when corrected for N intake (NI), N retention was similar (P > 0.05) across diets. This study suggests that alkaloids in LS restricted feed intake and limited ADG, but over the 90-d experimental period, lambs seemed to adapt the presence of alkaloids in LS. Key words: Lambs, lupin, alkaloids, growth, nitrogen balance
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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) | 0.001 | 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 | 0.001 |
| 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".