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Record W3163220302 · doi:10.3168/jds.2020-19672

Predicting ruminally undegraded and microbial protein flows from the rumen

2021· article· en· W3163220302 on OpenAlexaff
M.D. Hanigan, V.C. Souza, R. Martineau, Veridiana L. Daley, P.J. Kononoff

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsRumenForageFractionationAnimal scienceDegradation (telecommunications)Protein degradationChemistryNutrientBiologyChromatographyFood scienceBiochemistryAgronomyEcologyFermentation

Abstract

fetched live from OpenAlex

The objectives of the present work were (1) to identify the cause of the linear bias in predictions of rumen-undegradable protein (RUP) content of feeds, and devise methods to remove the bias from prediction equations, and (2) to further explore the impact of rumen-degradable protein (RDP) on microbial N (MiN) outflow from the rumen. The kinetic model used by NRC (2001), which is based on protein fractionation and rates of degradation (Kd) and passage (Kp), displays considerable slope bias (-0.30 kg/kg), indicating parameter or structural problems. Regressing Kp by feed class and a static adjustment factor for the in situ-derived Kd on observed RUP flows completely resolved the slope bias problem, and the model performed significantly better than models using unadjusted Kd and marker-based Kp. The Kd adjustment was 3.82%/h, which represents approximately a 50% increase in rates of degradation over the in situ values, indicating that in situ analyses severely underestimate true rates of protein degradation. The Kp for concentrate-derived protein was 5.83%/h, which was slightly less than the marker-predicted rate of 6.69%/h. However, the derived forage protein rate was 0.49%/h, which was considerably less than the marker-based rate of 5.07%/h. Compartmental analysis of data from a single study corroborated the regression analysis, indicating that a 25% reduction in the overall passage rate and an 87% increase in the rate of degradation were required to align ruminal N pool sizes and the extent of protein degradation with the observed data. Therefore, one must conclude that both the in situ-derived degradation rates and the marker-based particle passage rates are biased relative to protein passage and cannot be used directly to predict RUP outflow from the rumen. The effects of RDP supply on microbial nitrogen (MiN) flow were apparent when intakes of individual nutrients were offered but not when DM intake and individual nutrient concentrations were offered, due to collinearity problems. Microbial N flow from the rumen was found to be linearly related to ruminally degraded starch, ruminally degraded neutral detergent fiber (NDF), RDP, and forage NDF intakes; and quadratically related to residual OM intake. More complicated models containing 2- and 3-way interactions among nutrients were also supported by the data. Independent MiN responses to RDP, ruminally degraded starch, and ruminally degraded NDF aligned with the expected responses to each of those nutrients. Nonlinear representations of MiN were found to be inferior to the linear models. Despite using unbiased predictions of RUP and MiN as drivers of AA flows, predictions of Arg, His, Ile, and Lys flow exhibited linear slope bias relative to the observed data, indicating that representations of the AA composition of the proteins may be biased or the observed data are biased. This is an improvement over the NRC (2001) predictions, where bias adjustments were required for all of the essential AA. Despite the bias for 4 AA flows, the revised prediction system was a substantial improvement over the prior work.

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)0.002
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

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