173 The effects of Fescue EMTTM Mineral Defense on stocker heifer growth while grazing tall fescue
Bibliographic record
Abstract
Abstract Previous research demonstrates grazing tall fescue can decrease reproductive performance and weight gain in cattle. The objective of this study was to evaluate Fescue EMTTM Mineral Defense (Cargill Animal Nutrition, Minneapolis, MN) on summer weight gain in cattle grazing tall fescue pastures in SW Missouri. Heifers (n = 120; initial BW = 236 ± 2.5 kg) were stratified by weight to replicated tall fescue pastures to either a control mineral treatment or Fescue EMT™ Mineral Defense treatment. Forage availability was estimated weekly by ultrasonic sensor. Pasture samples were collected every 21 d and analyzed for ergovaline concentrations. Heifer weights and blood prolactin were measured throughout the trial. Average daily mineral consumption was calculated by mineral offered less residual. Data were analyzed on a pen-mean basis as a completely randomized design using JMP with 6 pens/ treatment and 10 heifers/pen. Prolactin was analyzed as Repeated Measures in JMP. Initial weights between treatments were not different (P > 0.05). Endophyte infection measured 75% or greater in all pastures. No differences were detected in pasture ergovaline (149 ± 19 µg/kg) or pasture availability (2,600 ± 150 kg/ha) between treatments (P > 0.20 at each sampling). Heifer ADG consuming Fescue EMT™ Mineral Defense compared to control mineral was greater at 0.28 kg versus 0.22 kg resulting in total gains of 21.8 kg versus 16.6 kg, respectively (P < 0.05). However, blood prolactin numerically decreased over time in both treatments. Results from this trial demonstrate a 31% improvement in weight gain for cattle consuming Fescue EMTTM Mineral Defense compared with cattle consuming a control mineral while grazing toxic tall fescue.
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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) | 0.000 | 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) | 0.001 | 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".