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Record W4391429056 · doi:10.1093/jas/skae031

Effect of water sulfate and dietary bismuth subsalicylate on feed and water intake, ruminal hydrogen sulfide concentration, and trace-mineral status of growing beef heifers

2024· article· en· W4391429056 on OpenAlexafffund
Mikaela G Evans, John C. Campbell, Gabriel O Ribeiro, Darren D Henry, Cheryl Waldner, G.B. Penner

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsAnimal scienceChemistrySulfateRandomized block designTrace mineralForageDry matterHydrogen sulfideAgronomyBiology

Abstract

fetched live from OpenAlex

In the Northern Great Plains, cattle may be exposed to water with an elevated sulfate concentration resulting in ruminal hydrogen sulfide (H2S) production and risk of copper deficiency. There are currently few strategies available to help mitigate effects arising from high-sulfate water (HS). The objective of this study was to evaluate the effects of feeding a moderate-forage diet with or without bismuth subsalicylate (BSS; 0.0% vs. 0.4% DM basis) when provided water with a low- (LS; 346 ± 13) or HS (4,778 ± 263 mg/L) concentration on feed and water intake, ruminal H2S concentration, and liver and serum trace-mineral concentrations. Twenty-four Limousin × Simmental cross beef heifers (221 ± 41 kg) were stratified based on initial liver Cu into a completely randomized block design with a 2 × 2 factorial treatment arrangement. Feed and water intake (measured weekly), ruminal H2S concentration (measured on days 42 and 91), liver (measured on days -13 and 91), and serum trace-mineral concentrations (measured on days 1, 28, 56, and 91) were evaluated. Initial liver trace-mineral concentrations were used as a covariate in the statistical model. Water intake tended to be reduced with the inclusion of BSS (P = 0.095) but was not affected by water sulfate (P = 0.40). Water sulfate and BSS did not affect dry matter intake (DMI; P ≥ 0.89). Heifers consuming HS had a ruminal H2S concentration that was 1.58 mg/L more (P < 0.001) than LS. The inclusion of BSS reduced (P = 0.035) ruminal H2S concentration by more than 44% (1.35 vs. 0.75 mg/L). Regardless of the water sulfate concentration, heifers fed BSS had lesser liver Cu concentration (average of 4.08 mg/kg) than heifers not provided BSS, and when not provided BSS, HS had lesser Cu than LS (42.2 vs. 58.3; sulfate × BSS, P = 0.019). The serum concentration of Cu did not differ over time for heifers not provided BSS; whereas, heifers provided BSS had lesser serum Cu concentration on day 91 than on days 28 and 55 (BSS × time, P < 0.001). The liver concentration of selenium was reduced (P < 0.001) with BSS inclusion but the selenium concentration in serum was not affected by sulfate, BSS, or time (P ≥ 0.16). BSS reduced ruminal H2S concentration, but depleted liver Cu and Se. Moreover, sulfate concentration in water did not appear to affect DMI, water intake, or growth, but increased ruminal H2S and reduced liver Cu concentration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.251
Teacher spread0.236 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes2
Has abstractyes

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