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Record W4388540008 · doi:10.1093/jas/skad281.556

PSVIII-A-1 Effect of Source of Trace Mineral and Bismuth Subsalicylate on Growth, Ruminal H2s, and Trace Mineral Status of Growing Heifers

2023· article· en· W4388540008 on OpenAlexaff
Mikaela G Evans, John Campbell, Gabriel O Ribeiro, Darren D Henry, Cheryl Waldner, Greg B Penner

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChemistryTrace mineralAnimal scienceDry matterTrace MineralsRandomized block designMethionineSeleniumFood scienceBiochemistryBiologyAgronomyAmino acid

Abstract

fetched live from OpenAlex

Abstract This study evaluated the effects of providing inorganic, chelated, and inorganic plus injectable trace mineral supplementation strategies with or without the inclusion of bismuth subsalicylate (BSS) on dry matter intake (DMI), water intake, ruminal hydrogen sulfide (H2S) concentration, and trace mineral status of growing beef heifers provided high sulfate (5,055 ± 228 mg/L) water. This study consisted of an 84-d feeding period with a 2×2 + 1 factorial treatment arrangement conducted using 2 blocks. Beef heifers (n = 15/block; 367 ± 33 kg BW) in each block were stratified according to their initial liver copper concentration. Mineral treatments included inorganic [added CuSO4 (7.76 mg/kg), ZnSO4 (22.92 mg/kg), MnSO4 (15.28 mg/kg)], chelated (100% of the Cu-methionine (7.68 mg/kg ), Zn-methionine (22.89 mg/kg), and Mn-methionine (15.27 mg/kg), and injectable trace mineral (15 mg/mL Cu, 10 mg/mL Mn, 60 mg/mL Zn) provided in combination with the inorganic mineral treatment. The BSS treatments were 0.0 (CON) or 0.2% (BSS) on a DM basis. Feed and water intake (weekly), ruminal H2S concentration (d 42 and 84), and liver (pre-study, d 42 and 84) and serum trace mineral concentrations (d 1, 28, 56, and 84) were evaluated. Initial liver trace mineral concentration was used as a covariate. Dry matter intake tended to be greater (mineral × BSS, P = 0.05) for heifers provided chelated minerals and fed CON, compared with BSS, and tended to be less for heifers fed inorganic and CON than BSS. Heifers fed chelated minerals drank 6.1 L/d more (P < 0.05) water than those provided inorganic minerals. Injectable trace-mineral provision did not affect DMI or water intake. Ruminal H2S was not affected by mineral type or BSS (P ≥ 0.33). The inclusion of BSS reduced (P < 0.01) liver Cu concentration from 60.5 to 31.3 ppm. Heifers provided the injectable minerals had greater (P < 0.01) liver Cu concentration than the inorganic without BSS treatment. Serum copper was not affected by BSS or mineral treatment (P ≥ 0.44). The liver concentration of Se was reduced (P < 0.01) by the inclusion of BSS. The serum Se concentration was not affected by mineral type, BSS, time, or any two- or three-way interactions (P ≥ 0.24). Bismuth subsalicylate did not affect ruminal H2S, and negatively affected liver Cu concentration. The use of organic trace mineral supplementation strategies did not affect the trace mineral status of beef heifers drinking high sulfate water, but use of an injectable trace mineral supplement increased liver Cu.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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