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Record W4311945098 · doi:10.3390/agriculture12122168

The Effect of Increasing Dietary Manganese from an Organic Source on the Reproductive Performance of Sows

2022· article· en· W4311945098 on OpenAlexaff
Clint E. Edmunds, Alyssa S. Cornelison, Chantale Farmer, Christof Rapp, Valerie E. Ryman, Wes P. Schweer, Mark Wilson, C. R. Dove

Bibliographic record

VenueAgriculture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsWeaningLactationAnimal scienceRandomized block designBiologyParity (physics)PregnancyAgronomy

Abstract

fetched live from OpenAlex

The objective of this study was to determine the effect of dietary manganese on the reproductive performance of sows. Sows (n = 39; 231 ± 8 kg) were randomly assigned to one of three dietary levels of supplemented Mn (CON: 0 ppm Mn; PRO20: 20 ppm Mn; PRO40: 40 ppm Mn). The experimental treatments were initiated at breeding and continued through two parities. The sows were blocked by parity within each farrowing group. The data were analyzed as a randomized complete block design using the MIXED procedure of SAS with diet as a fixed effect and block as a random effect. The lactation feed intake increased in the PRO20 sows compared to the CON and PRO40 sows (p < 0.05). The PRO20 and PRO40 sows farrowed piglets with improved average daily gain from birth to weaning (CON 214 g/day; PRO20 237 g/day; 220 g/day; p < 0.05) compared to the CON sows. The milk fat content was lower in the PRO20 (5.5%) and PRO40 sows (6.1%; p < 0.05) compared to the CON sows (7.8%), possibly due to increased milk demand. Supplementary dietary Mn throughout two gestation and lactation cycles led to improved birth weights and pre-weaning growth of piglets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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 teacher head, 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

Citations8
Published2022
Admission routes1
Has abstractyes

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