Liquid hot water treatment of rice straw enhances anaerobic degradation and inhibits methane production during in vitro ruminal fermentation
Bibliographic record
Abstract
Liquid hot water (LHW) treatment can be used to disrupt the fiber structure of rice straw. This in vitro ruminal batch culture study investigated the effect of LHW treatment on feed degradation, methane (CH 4 ) production, and microbial populations. Rice straw was treated by LHW, and in vitro ruminal fermentation was performed using an automatic system with 72 h of incubation. Scanning electron microscopy showed that LHW treatment disrupted the physical structure of rice straw. Liquid hot water treatment decreased neutral detergent fiber and hemicellulose contents of the rice straw and increased neutral detergent solubles, water-soluble carbohydrates, and arabinose contents. Liquid hot water treatment increased dry matter degradation and volatile fatty acid concentration and decreased the acetate:propionate ratio, CH 4 production, hydrogen accumulation, neutral detergent fiber degradation, and populations of protozoa, fungi, and cellulolytic bacteria. In summary, LHW treatment disrupted the cellulose-hemicellulose-lignin structure matrix of rice straw, leading to increased substrate degradability and decreased CH 4 production. Therefore, the LHW treatment is a potential strategy to improve the nutritive value of forage such as rice straw and decrease the CH 4 emissions in ruminants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".