Contribution of Urine and Dung Patches from Grazing Sheep to Methane and Carbon Dioxide Fluxes in an Inner Mongolian Desert Grassland
Bibliographic record
Abstract
The effects of sheep urine and dung patches on methane (CH4) and carbon dioxide (CO2) fluxes were investigated during the summer-autumn in 2010, to evaluate their contribution to climate change in a desert grassland in Inner Mongolia, China. Results indicate that the cumulative CH4 emissions for dung patches, urine patches and control plots were - -0.076, -0.084, and -0.114 g/m(2) and these were net CH4 sinks during the measured period. The level of CH4 intake from urine and dung plots decreased 25.7%, and 33.3%, respectively, compared with a control plot. CO2 fluxes differed (p<0.01) in urine plots, with an average of 569.20 mg/m(2)/h compared with control plots (357.62 mg/m(2)/h) across all sampling days. Dung patches have cumulative CO2 emissions that were 15.9% higher compared with the control during the 55-d period. Overall, sheep excrement weakened CH4 intake and increased CO2 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".