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Record W4388181626 · doi:10.1093/jas/skad341.093

127 How to Best Calculate Acid Binding Capacity at pH 4 in Feed Ingredients for Weanling Pigs

2023· article· en· W4388181626 on OpenAlexaboutno aff
N. A. Gutierrez, Noud Aldenhoven, Maarten J. Scholtes-Timmerman, Neil Jaworski

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsWeanlingSoybean mealFeed conversion ratioMealAnimal feedChemistryFood scienceAnimal scienceBiotechnologyMathematicsBiologyBody weightRaw material

Abstract

fetched live from OpenAlex

Abstract The value of acid binding capacity at pH 4 (ABC-4) of feed ingredients can be used to formulate weanling pig diets for improved feed efficiency and free of ZnO. In practice, it is expensive and time consuming to analyze all ingredients used in pig diet formulation. Therefore, the objective of this study was to determine the most efficient and cost-effective method to accurately predict ABC-4 in feed ingredients used in weanling pig diets. Variation of the ABC-4 value is low in most feed ingredients, and the average value of ABC-4 is accurate enough for use in feed formulation [Lawlor et al. (2005)]. However, limestone and soybean meal (SBM) were studied because of their contribution to dietary ABC-4, and their inherit variability in ABC-4. Results showed that when ABC-4 of limestone and SBM is analyzed, the error in ABC-4 of final feed decreases by 58%. Therefore, if SBM and limestone are analyzed for ABC-4, and fixed values are used for all other ingredients, the value of ABC-4 in the final diet will be +/-20 mEq/kg of the target ABC-4 (P < 0.05). When ingredients are not analyzed for ABC-4, the variability increases and value in final feed will vary between +/- 48 mEq ABC-4/kg (P < 0.05). Next, 39 samples of SBM were collected from the US, Brazil, and Argentina to develop a prediction equation of ABC-4 for SBM from its macro-nutrient composition. Values of ABC-4 of SBM ranged between 773 and 878 mEq/kg, with an average of 824 mEq/kg and STD of 28 mEq ABC-4/kg. No significant relationships between the analyzed macro-nutrients and ABC-4 of SBM were observed (R2 = 0.01, with cross-validation). Therefore, the accuracy and precision of predicting ABC-4 of SBM were not improved by developing a derivative formula to estimate ABC-4 in SBM from its macro-nutrients. Next, 24 samples of limestone were collected from Canada to develop methods to predict ABC-4 accurately and quickly. The value of ABC-4 ranged between 20,823 and 23,412 mEq/kg, with an average of 22,117 mEq/kg and STD of 431 mEq/kg. A good correlation was obtained between ABC-4 and near infrared spectroscopy calibration (NIR) lines (R2 = 0.79, unground, with cross-validation). We conclude that for accurate and rapid determination of ABC-4, and ease of application in nursery diet formulation, ABC-4 can be analyzed in limestone using NIR, in SBM using wet chemistry, and for all other ingredients fixed values can be used. Additionally, for accurate formulation of nursery diets, a safety margin of 20 mEq/kg can be applied to the target ABC-4 value in the final diet.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.079
GPT teacher head0.289
Teacher spread0.210 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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