Rumen degradation of oil palm fronds is improved through pre-digestion with white rot fungi but not through supplementation with yeast or enzymes
Bibliographic record
Abstract
Hassim, H. A., Lourenço, M., Goh, Y. M., Baars, J. J. P. and Fievez, V. 2012. Rumen degradation of oil palm fronds is improved through pre-digestion with white rot fungi but not through supplementation with yeast or enzymes. Can. J. Anim. Sci. 92: 79–87. Rumen fermentation kinetics of oil palm fronds (OPF) supplemented or not with enzymes (Hemicell® or Allzyme SSF®) or yeasts (Levucell®SC or Yea-Sacc®) were studied through an in vitro gas production test (96 h) (exp. 1). In exp. 2, enzymes were supplemented to OPF pre-treated during 3 or 9 wk with either one of five white rot fungi strains. Yeasts and enzymes were tested both in active and inactive forms, which revealed the most appropriate set-up to distinguish between the rate of supplements as direct contributors to the fermentation substrate vs. stimulators of the fermentation of the basal substrate. In exp 1, addition of active and inactive Yea-Sacc® increased the apparently rumen degradable carbohydrates (ARDC) by 11%, whereas enzymes did not affect rumen degradability of non-inoculated OPF. Neither yeast nor enzymes influenced the rate of gas production of non-inoculated OPF, except for active Hemicell® at the low dose. In exp. 2, inoculation of OPF with Ceriporiopsis subvermispora for 3 wk and Lentinula edodes for 9 wk increased ARDC, but additional enzyme supplementation did not further improve ARDC or the rate of gas production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".