Technical note: Correction of net portal absorption of nitrogen compounds for differences in methods: First step of a meta-analysis
Bibliographic record
Abstract
The objective of this study was to evaluate the usefulness of correcting net portal absorption (NPA) of urea-N, ammonia, and AA-N for differences in methods before their inclusion into a meta-analysis. It was hypothesized that the difference, or portal-drained viscera (PDV) balance, between N inputs (apparently digested N plus urea-N) and outputs (ammonia plus AA-N) was 0 in the absence of measurement errors and based on the assumption that other sources of N inputs and outputs were relatively small and balanced each other. A database was built from 44 publications that reported data from 129 treatments (sheep, n = 71; beef cattle, n = 32; and dairy cows, n = 26). When necessary, NPA of urea-N (n = 38) and ammonia (n = 35) results were recalculated on a whole-blood basis, whereas NPA of AA-N (n = 87) was recalculated for all the N from AA transferred across the PDV rather than only the N from the alpha-amino group. Before corrections, PDV balance averaged 22.9% of N ingested (SD 29.0) for all treatments; after corrections, PDV balance significantly decreased to 10.2% of N ingested (SD 34.7). No difference in PDV balance was observed among species before or after corrections. Correcting NPA of urea-N, ammonia, and AA-N increased the accuracy without improving precision. Therefore, from a biological perspective, recalculating reported data seems appropriate to reduce bias due to differences in methods because this approach reduces the excess in N inputs relative to N outputs.
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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 | 0.205 | 0.427 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.035 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".