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Record W3032963311 · doi:10.1139/cjas-2019-0221

Effects of dietary kelp (<i>Ascophylum nodosum</i>) supplementation on survival rate and reproductive performance of mink challenged with Aleutian mink disease virus

2020· article· en· W3032963311 on OpenAlexaffvenue
A. Farid, Nancy J. Smith

Bibliographic record

VenueCanadian Journal of Animal Science · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsAgriculture and Agri-Food CanadaDalhousie University
Fundersnot available
KeywordsMinkBiologyKelpAnimal scienceLitterWeaningAmerican minkSurvival rateVeterinary medicineInternal medicineEcologyMedicine

Abstract

fetched live from OpenAlex

Infection with Aleutian mink disease virus (AMDV) has negative effects on reproductive performance and survival rate of American mink (Neovison vison). The objectives of this study were to assess the effects of kelp (Ascophylum nodosum) supplementation on survival, growth rate, and reproductive performance of mink challenged with AMDV. AMDV-free female black mink (n = 75) were intranasally inoculated with a local AMDV strain. Mink were fed a commercial pellet supplemented with 1.5% or 0.75% kelp or were kept as controls (received no kelp) for 451 d. Body weight and rectal temperature were recorded on days 0, 31, 56, 99, 155, 366, and 451 post inoculation (PI). Annual mortality rates were 13.6%, 20.0%, and 31.8% for mink fed 1.5%, 0.75%, or 0.0% kelp, respectively (P = 0.29). Mink which were fed 1.5% kelp had a significantly (P &lt; 0.01) greater daily weight loss during breeding and post-breeding periods (days 155–366 PI), and outperformed (P &lt; 0.01) the other groups in regard to litter sizes at birth and weaning. Differences among treatments were not significant for the number of females mated, or whelped of those exposed to males, kit survival from birth to weaning, or rectal temperature. It was concluded that 1.5% kelp supplementation had beneficial effects on survival rate of adult mink and litter size.

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)0.001
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 score0.631
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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