Comparison of two variants of the Fick principle for estimation of mammary blood flow in dairy cows fed two levels of dry matter intake
Bibliographic record
Abstract
The estimation of the mammary blood flow (MBF) is an important component of studies of the utilization of metabolites by the mammary gland of lactating animals. Although there are several flow meters available to measure MBF by direct contact with the vessels perfusing the mammary gland, their use is limited mainly by the surgical preparation required for their implantation. For that reason, the application of the Fick principle was assessed as a mean to estimate MBF as part of a study on amino acid utilization by the mammary gland in three pasture-fed dairy cows at either ad libitum or restricted dry matter intakes. Two methods based on the Fick principle were assessed, namely, arterio-venous differences of amino acids (methionine: Met or phenylalanine+tyrosine: Phe + Tyr) and tritiated water (TOH). The estimated MBF was not significantly different for the Met and Phe + Tyr methods (average of 8.1 and 8.8 L min–1, respectively). The TOH method yielded a significantly lower (P < 0.05) estimate of blood flow (average of 5.3 L min–1). Using the Met and Phe+Tyr methods, the MBF was lower during the period of dietary restriction compared with the ad libitum treatment (average of 9.4 and 7.5 L min–1, respectively). In contrast, the TOH method resulted in a numerically higher MBF for the restricted group (5.7 vs. 4.9 L min–1). The short sampling period a nd the loss of indicator in the TOH method appear to be a disadvantage for extrapolating the estimated values to balance studies with lactating cows involving longer periods of time. The estimated values obtained using the Met or Phe + Tyr appear to be re presentative of the MBF during the experimental period. Therefore, any of these methods (Met, Phe + Tyr) may be used alone or in combination as an alternative to flow meters in studies of mammary metabolism. Key words: Dairy cows, mammary blood flow, Fick principle
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".