Repeatability and variability of short-term spot measurement of methane and carbon dioxide emissions from beef cattle using GreenFeed Emissions Monitoring System
Bibliographic record
Abstract
The purpose of this study was to determine the repeatability of methane (CH4) and carbon dioxide (CO2) emissions from beef cattle using GreenFeed emissions monitoring (GEM) system and as affected by sampling frequency and measurement periods. Twenty-eight crossbred replacement beef heifers were monitored using the GEM system over 59 d to collect their CH4 and CO2 emissions data. Heifers’ feed intake was recorded by eight automated feeding stations. The standardized dry matter intake (SDMI), CH4 and CO2 emission and yield (g kg−1 SDMI) were averaged over 1, 3, 7, and 14 d periods. On average, animals emitted 204.7 g d−1 (SD = 36 g d−1) and 6408 g d−1 (SD = 780 g d−1) of CH4 and CO2, respectively. Between-animal coefficients of variation for all variables decreased with an increasing averaging period (from 1 to 14 d). The coefficient of determination (R2) between CH4 emission and SDMI was increased from 0.25 to 0.73 as averaging period increased from 1 to 14 d. Similarly, the R2 between CO2 emission and SDMI increased from 0.39 to 0.79 as averaging period increased from 1 to 14 d. It was determined that averaging over 7 to 14 d with minimum of 20 spot samples was needed to produce repeatable and reliable averaged CH4 and CO2 emissions and correlated with SDMI.
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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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".