MétaCan
Menu
← Back to cohort
Record W4366768808 · doi:10.1139/cjz-2023-0006

Relationship between food grinding and gut microbiota in Brandt's voles

2023· article· en· W4366768808 on OpenAlexvenueno aff
Qiu-Yi Shen, Jiayi Shi, Ke-Han Gu, Wanhong Wei, Shengmei Yang, Xin Dai

Bibliographic record

VenueCanadian Journal of Zoology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyFecesGut floraGrindingStreptococcusFood scienceFood consumptionEcologyMicrobiologyZoologyBacteriaImmunology

Abstract

fetched live from OpenAlex

Food grinding is an abnormal behavior in rodents and its influencing factors are unknown. Our study investigated the potential relationships between gut microbiota and food grinding in Brandt's voles ( Lasiopodomys brandtii (Radde, 1861)) by comparing the differences between groups with different degrees of food grinding. The strong food-grinding group showed more relative food ground, higher ratio of ground food to food consumption, and lower percentage of time spent in the central area. The structure of fecal microbiota community differed between the strong and weak food-grinding groups. Strong and weak food-grinding voles showed higher abundances of Alistipes and Aerococcus and Atopostipes, Paenalcaligenes, Un—s-Clostridiaceae bacterium GM1, and Streptococcus, respectively. Strong correlations between the food ground to consumption ratio and abundances of fecal microbiota were found in Streptococcus and Paenalcaligenes. Fecal acetate and isobutyrate contents were higher in strong food-grinding voles and positively correlated with relative ground food and food ground to consumption ratio. Our study suggests that gut microbiota and short-chain fatty acids may contribute to the regulation of food-grinding behavior.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.262
Teacher spread0.232 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Zoology→Same topicGut microbiota and health→French-language works237,207→