Risk analysis of global warming-induced greenhouse GAS emissions from natural sources
Bibliographic record
Abstract
The increase in the emissions of greenhouse gases (GHG) CO 2 , CH 4 , and N 2 O is the most important factor causing global warming.Natural sources make up about 96%, 46%, and 64% of total emissions of the three gases, respectively.Relatively small man-made CO 2 fluxes, together with CH 4 and N 2 O (with a radiative force 34 and 298 times higher than that of CO 2 , respectively) upset the natural balance of the carbon (C) cycle and create an artificial forcing of global temperatures which is warming the planet.However, even after stopping all anthropogenic CO 2 emissions, the warming-induced GHG from natural sources will cause an on-going temperature increase and many resulting environmental problems.Based on literature, we analyse the potential change in GHG emissions from the main natural sources, which are influenced by the effects of global warming.Since there are various uncertainties in the estimations of terrestrial-atmosphere and ocean-atmosphere CO 2 exchange, this most important factor remains un-predicted and needs significantly more investigation of the ability of oceans and terrestrial ecosystems to absorb CO 2 .Both CH 4 and N 2 O emissions may continue to increase.The thawing of CH 4 hydrates in the ocean shelf and in permafrost regions is the largest long-term threat for global warming, but even now rising temperature will enhance emissions from wetlands, lakes, vegetation and even upland soils, due to an increasing threat of wildfires.Changes in hydrological regime are the main driving force for N 2 O emissions.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".