Evaluation of Seaweed-Based Feed Additive on Enteric Methane Emissions of Grazing Heifers
Bibliographic record
Abstract
This study evaluated the impact of a seaweed-based feed additive (SBFA) on enteric methane emissions in grazing heifers. Two groups of heifers (n = 11 per group) were maintained on tame pasture under identical conditions. The trial consisted of three phases: a two-week baseline period, a two-week adaptation period, and a seven-week full-dose period. During adaptation, the treatment group received SBFA once daily, with the dosage gradually increased to a target dose of 280 mg bromoform/head per day. This full dose was administered throughout the final phase. Enteric emissions of methane were continuously monitored using the GreenFeed emission monitoring system. During the baseline period, gas emissions were not different between the groups (p = 0.75); however, during the adaptation (p = 0.08) it tended to be lower in the SBFA group compared to the control, and during the full-dose period, methane emissions in the SBFA treatment group were significantly (p < 0.01) lower than in the control group (p < 0.01), averaging 53.7 g/d versus 203.2 g/d, corresponding to a 73.6% reduction in methane. Additionally, a prolonged suppression effect was observed, with methane emissions in the treatment group remaining low for three days after removal of the SBFA compared to the control group (p < 0.01), and on day 4 after the removal, the SBFA treatment group still tended (p = 0.07) to be lower than the control group. These findings indicate that SBFA, when administered once daily, has significant potential for mitigating enteric methane emissions in grazing cattle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".