Effect of iron bioavailability on performance, feed digestibility, blood parameters, and carcass characteristics at slaughter of grain-fed veal calves
Bibliographic record
Abstract
The effects of the iron chelator, EDTA, on performance, feed digestibility, blood parameters, and carcass quality were investigated in 48 veal calves (52 ± 4 kg BW) allocated to five treatment groups: a control, 5 mg EDTA mg-1 Fe in the feed for the last 8 or 18 wk before slaughter, or 15 mg EDTA mg-1 Fe in the feed for the last 8 or 18 wk before slaughter. Calves were fed a diet of whole corn and protein supplement (grain: supplement ratio of 2:1, 3.5:1 and 5:1 for starter, grower, and finisher production phases, respectively, resulting in crude protein levels of 21, 18 and 15%, respectively). The effects of both dose and duration of EDTA administration were analysed using a randomised complete block design in a factorial model. Daily DMI and ADG decreased linearly with an increasing dose of EDTA (P < 0.001). ADG declined linearly with increasing dose and duration of EDTA administration (P < 0.05). Blood iron, haemoglobin and haematocrit levels decreased linearly with increasing EDTA dose (P < 0.001) and duration of administration (P < 0.05). Carcass weight (kg) and yield (%) decreased linearly with both increasing EDTA dose (P < 0.001) and duration of EDTA administration (P < 0.05). Carcass classification improved linearly with increasing EDTA dose (P < 0.01). When EDTA is used as a dietary supplement for veal calves, there may be beneficial effects on carcass classification, but the dose and duration of administration need to be chosen judiciously to avoid deleterious effects on the well-being of the calves. Key words: Veal, iron bioavailability, EDTA, digestibility, haemoglobin
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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 | 0.001 |
| 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 | 0.001 | 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".