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Record W1503341865 · doi:10.1111/asj.12196

Determination of the net energy content of canola meal from <i><scp>B</scp>rassica napus</i> yellow and <i><scp>B</scp>rassica juncea</i> yellow fed to growing pigs using indirect calorimetry

2014· article· en· W1503341865 on OpenAlexafffund
Jung Min Heo, Martin Nyachoti

Bibliographic record

VenueAnimal Science Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
FundersCanola Council of Canada
KeywordsCanolaBrassicaMealNet energyChemistryAnimal scienceDry matterCalorimetryFood scienceBiologyBotany

Abstract

fetched live from OpenAlex

The net energy (NE) content of canola meals (CM; i.e. Brassica napus yellow and Brassica juncea yellow) in growing pigs was determined using an indirect calorimetry chamber or published prediction equations. The study was conducted as a completely randomized design (n=6), with (i) a basal diet and (ii) 2 diets containing 700 g/kg of the basal diet and 300 g/kg of either of the two varieties of CM. A total of 18 growing barrows were housed in metabolism crates for the determination of digestible (DE) and metabolizable (ME) energy. Thereafter, pigs were transferred to the indirect calorimetry chamber to determine heat production (HP). The NE contents of diets containing Brassica napus yellow and Brassica juncea yellow determined with the direct determination technique and prediction equations were 9.8 versus 10.3 MJ/kg dry matter (DM) and 10.2 versus 10.4 MJ/kg DM, respectively. Retained energy (RE) and fasting heat production (FHP) of diets containing Brassica napus yellow and Brassica juncea yellow were 5.5 versus 5.7 MJ/kg and 4.3 versus 4.5 MJ/kg, respectively, when measured with the direct determination technique and prediction equations. The NE contents of Brassica napus yellow and Brassica juncea yellow were determined to be 8.8 and 9.8 MJ/kg DM, respectively, using the direct determination technique.

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)0.001
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 score0.628
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.227
Teacher spread0.198 · 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

Citations28
Published2014
Admission routes2
Has abstractyes

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