Greenhouse Gas Emissions from Livestock Manure Management in Southwestern Siberia, Russia
Bibliographic record
Abstract
This study investigates the amounts of greenhouse gas (GHG) emissions due to manure handling within different livestock production systems in Tyumen oblast of Western Siberia. Tyumen oblast occupies approx. 160 000 km² of Asian taiga and forest steppe. The amount of GHGs from manure was calculated as a function of the handling according to current IPCC guidelines for ecozones and livestock production systems. The entire Tyumen oblast has annual 7 400 t methane emissions and 440 t nitrous oxide emissions from manure. Three livestock production systems are prevalent in Tyumen oblast: Mega farms, small farms and peasant farms. The share of mega farms is 81 % (171 kt CO2 eq). Additionally, the slurry system in mega farms causes environmental pollution. GHG emissions and environmental pollution could be reduced by implementing solid manure systems or pasturing, by installing storage facilities for slurry outside the stables and through application of the manure as fertiliser at mega farms. In small farms solid manure systems and a small stocking density of livestock lead to smallest GHG emissions (1 %, 3 kt CO2 eq) from manure. In peasant farming 18 % (38 kt CO2 eq) of GHGs are emitted due to pasturing.
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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 | 0.001 | 0.001 |
| 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".