Effects of neutral detergent fiber digestibility estimation method on calculated energy concentration of canola meals from twelve Canadian processing plants
Bibliographic record
Abstract
Our aim was to determine whether the method used to estimate truly digestible neutral detergent fiber (tdNDF) affects calculated concentrations of total digestible nutrients (TDN1x) and net energy of lactation (NEL3x) of canola meal (CM). Samples were collected from 12 CM processing plants in Canada over 4 yr (2011 to 2014, n = 47) and analyzed for dry matter (DM), crude protein (CP), ether extract (EE), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), lignin (ADL), and neutral detergent insoluble CP (NDICP). Ruminal in situ incubation of CM samples was performed at 0, 24, 48, 96, and 288 h to determine NDF fractions (A, B, and C), effective ruminal NDF digestibility (ERNDFD), and indigestible NDF (iNDF) of CM. Three tdNDF-estimation methods were evaluated: 1) National Research Council (NRC) = 0.75 × (NDF - NDICP - ADL) × {1- [ADL/ (NDF - NDICP)]0.667}; 2) iNDF = 0.75 × (NDF - NDICP - NDF remaining after 288 h in situ); and 3) ERNDFD estimated from in situ NDF digestion kinetics. Resulting tdNDF values were used for calculation of TDN1x and NEL3x according to NRC (2001) equations. Data were analyzed with MIXED procedure of SAS 9.4 to determine the effect of processing plant on chemical composition, NDF degradation kinetics and NEL3x of CM. Effect of tdNDF estimation method on calculated TDN1x and NEL3x of CM was also evaluated. Model for analysis of processing plant included the fixed effect of plant and the random effect of year (plant) as replication, while analysis of tdNDF methods included the fixed effect of tdNDF estimation method and the random effects of processing plant and of year(plant) as replication. There was an effect of processing plant on DM (P = 0.03), CP (P < 0.01), EE (P < 0.01), and NDF (P < 0.01) of CM. Processing plant also had an effect on NDF fractions A (P < 0.01) and B (P = 0.02) but did not affect fraction C and ERNDFD. The tdNDF estimation method had an effect on tdNDF (P < 0.01), TDN1x (P < 0.01), and NEL3x (P < 0.01) of CM, yielding average NEL3x values of 1.72, 1.87, and 2.07 Mcal/kg for NRC, iNDF, and ERNDFD, respectively. Our results indicate that calculated energy concentration of CM according to NRC (2001) equations varies depending on the method used for estimation of tdNDF. Further research will be needed to determine the most accurate estimation method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".