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Record W3135206671 · doi:10.3390/ani11030711

Effects of Supplemental Feed with Different Levels of Dietary Metabolizable Energy on Growth Performance and Carcass Characteristics of Grazing Naturalized Swan Geese (Anser cygnoides)

2021· article· en· W3135206671 on OpenAlexaff
Yi Fang, Hai Xiang, Chengzhen Zhao, Daowei Zhou, Fujiang Hou, Yan D. Niu, Rongzhen Zhong

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGrazingAnimal scienceBiologyPastureEcology

Abstract

fetched live from OpenAlex

Grazing Swan geese (Anser cygnoides) have good meat quality but grow slowly. This study aimed to study whether supplemental feeding could improve growth performance of grazing Swan geese and investigate a suitable dietary metabolizable energy (ME) level of supplemental diet for grazing Swan geese. Naturalized healthy male Swan geese (n = 144; 42 ± 2.0 days and 1.21 ± 0.17 kg) were randomly allocated into 4 groups and grazed on pasture alone (control, CON) or offered supplemental diets with ME of 9.5, 11.5, or 13.5 MJ/kg of DM after grazing. Growth performance and body-size measurements (including bone development) were lower (p < 0.05) in CON versus supplemented geese, as well as slaughter measurements on days 28 and 56. The DM intake linearly decreased (p < 0.01) with increasing dietary ME from day 29 to 56. Slaughter, semi-eviscerated, eviscerated, and thigh muscle yield linearly (p < 0.01) decreased with increasing dietary ME on day 56. Lightness (L*) and yellowness (b*) for breast and thigh muscle on days 28 and 56, and breast muscle shear force on day 56, were lower (p < 0.01) in supplemented versus CON geese. In conclusion, supplemental feeding improved growth performance and carcass characteristics of grazing Swan geese, and supplemental feed with ME of 9.5 MJ/kg of DM could be offered to improve growth and meat quality of grazing Swan geese.

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 score0.572
Threshold uncertainty score0.210

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 studies0.0000.000
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 head0.013
GPT teacher head0.208
Teacher spread0.195 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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