Objective Bayesian vs. least squares estimation for by-products degradability with different rumen fluids
Bibliographic record
Abstract
The degradation kinetic curves of different by-products have been obtained. The considered by-products were lemon and several types of treated and untreated barley straw, and they were degraded by in vitro incubation with rumen fluid extracted from two herds of Murciano-Granadina goats, one of them fed alfalfa hay and the other one fed barley straw. The feeds were incubated at 39ºC for 12, 24, 36, 48 and 72 hours with each rumen fluid. The resulting fitted exponential-type degradation curves obtained with a frequentist statistical analysis were compared with those resulting from an objective Bayesian statistical analysis. The use of the objective Bayesian analysis smoothed the estimates of the frequentist fit using least squares, which did not suitably process the involved restrictions and avoided biologically unacceptable results. On the other hand, the rumen fluid from goats fed alfalfa hay fomented the greatest effective degradability and the degradabilities of the different by-products were also compared, with the result that the lemon by-product was the best degraded one under both statistical analyses. Key words: In vitro fermentation, by-product, degradation curve
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".