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Record W4414830636 · doi:10.1093/jas/skaf300.682

PSIX-24 The effects of camelina meal in western Canadian winter-feeding diets on feed intake and growth performance of replacement beef heifers.

2025· article· en· W4414830636 on OpenAlexaffabout
Stephanie A. Terry, Melissa Williams, Gabriel O Ribeiro, Gleise da Silva, Katharine M Wood

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of GuelphUniversity of AlbertaUniversity of SaskatchewanAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCamelinaCanolaMealDry matterCamelina sativaRapeseedCattle feedingCompletely randomized design

Abstract

fetched live from OpenAlex

Abstract This study aimed to evaluate the effects of camelina meal supplementation in western Canadian winter-feeding diets on dry matter intake (DMI) and growth performance of replacement beef heifers. One hundred and one crossbred replacement beef heifers were used in a completely randomized design and randomly assigned to one of four dietary treatments. The treatments assigned to each pen included; CON: the basal diet (60% grass hay, 15% barley straw, 10% corn silage, 10% barley grain, 5% supplement; dry matter (DM) basis, CANOLA: basal diet with canola meal replacing the 10% barley grain, MIX: basal diet with 5% canola meal and 5% camelina meal replacing 10% of the barley grain, and CAMELINA: basal diet with camelina meal replacing 10% of barley grain. The experiment consisted of a 28-d baseline measurement period where all pens received the CON diet, followed by two consecutive 42-d dietary feeding periods. Body weight was measured on two consecutive days at the beginning and end of the experiment and once at the beginning and end of each feeding period. Feed was provided ad libitum and feed intake was individually measured using the GrowSafe feed system. Cattle and their respective treatments were shuffled between pens once every week to ensure all cattle and all treatments were represented in each pen for every period. Statistical analysis was performed using the GLIMMIX procedure of SAS (SAS Institute Inc., Cary, NC, USA) with treatment as a fixed effect, and heifer as the experimental unit. During the first feeding period, CAMELINA cattle had lower (P < 0.01) dry matter intake (DMI) than all other treatments; however, there was no difference in average daily gain (ADG; P = 0.07) among treatments. The gain to feed ratio (GF) during the first feeding period was greater (P < 0.01) for CANOLA compared to MIX and CAMELINA, with CON being similar to all treatments. Body weight gain (BWG) was greatest for MIX and lowest for CANOLA (P = 0.05). In the second feeding period, treatment did not affect DMI (P = 0.25), while ADG was lower (P = 0.04) for CON compared to CANOLA. In the second period, GF was lower (P < 0.01) for CON than MIX, and BWG was greatest for CAMELINA and lowest for CON (P = 0.04). In conclusion, feeding camelina meal at 5% dietary DM into a high roughage diet improved GF, whilst feeding camelina at 10% DM initially reduced DMI but did not affect GF. These results suggest that camelina meal can replace canola meal in high roughage diets without adversely affecting growth performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.484

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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