Modeling portal-drained viscera and liver fluxes of essential amino acids in dairy cows
Bibliographic record
Abstract
The objective of this work was to predict essential amino acid (EAA) use and release by the portal-drained viscera (PDV) and liver of dairy cows. Previously derived equations were tested using data assembled from the literature, refit to the data, and modifications were undertaken to determine the best model for each EAA. The refitted model has the same structure as the original equations but is parameterized using a database of group means, as the original equations were derived using a single study with individual cow data and found to be biased. The PDV clearance model predicted portal vein concentrations given inputs of absorbed and arterial fluxes of EAA with root mean squared errors (RMSE) ranging from 3.3 to 12.1% of the observed means, and concordance correlation coefficients (CCC) ranging from 0.86 to 0.99 when using previously reported parameters. The reparameterized model generated from the assembled data set resulted in predictions of EAA portal vein concentrations with RMSE ranging from 3.2 to 8.6% and CCC ranging from 0.93 to 1.00. Slope bias ranged from 12.4 to 55.3% of mean squared errors and was correlated with arterial EAA concentrations. Modifying the model to allow rate constants to vary as a function of arterial EAA concentrations reduced slope bias, resulting in RMSE ranging from 1.9 to 6.5% and CCC from 0.97 to 1.00. Alternatively, splitting the model to account for use of EAA from absorption separately from arterial use resulted in poorer predictions and biologically infeasible parameter estimates. The liver clearance model predicted hepatic vein concentrations from arterial and portal vein input fluxes with RMSE across EAA ranging from 1.9 to 6.8% and CCC ranging from 0.97 to 1.00 when using reported parameters. The reparameterized model generated from the assembled data set resulted in predictions of EAA hepatic vein concentrations with RMSE ranging from 1.9 to 6.7% and CCC ranging from 0.97 to 1.00. Significant slope bias was present for Arg, His, Lys, Phe, Thr, and Val. Altering the model to represent the clearance rate constant as a function of arterial concentrations resulted in RMSE ranging from 1.8 to 6.5% and CCC ranging from 0.97 to 1.00. The combination of PDV and liver clearance models provided predictions of total splanchnic use similar to those of an empirical model representing splanchnic use as a fractional proportion of absorption that had RMSE ranging from 3.0 to 8.6% and CCC ranging from 0.95 to 0.99, with significant slope bias for the majority of EAA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".