Global Inventory of Gas Geochemistry Data from Fossil Fuel, Microbial and Burning Sources, version 2017
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. The concentration of atmospheric methane (CH4) has more than doubled over the industrial era. To help constrain global and regional CH4 budgets, inverse (top-down) models incorporate data on the concentration and stable carbon (δ13C) and hydrogen (δ2H) isotopic ratios of atmospheric CH4. These models depend on accurate δ13C and δ2H end-member source signatures for each of the main emissions categories. Compared with meticulous measurement and calibration of isotopic CH4 in the atmosphere, there has been relatively less effort to characterize globally representative isotopic source signatures, particularly for fossil fuel sources. Most global CH4 budget models have so far relied on outdated source signature values derived from globally nonrepresentative data. To correct this deficiency, we present a comprehensive, globally representative end-member database of the δ13C and δ2H of CH4 from fossil fuel (conventional natural gas, shale gas, and coal), modern microbial (wetlands, rice paddies, ruminants, termites, and landfills and/or waste) and biomass burning sources. Gas molecular compositional data for fossil fuel categories are also included with the database. The database comprises 10 706 samples (8734 fossil fuel, 1972 non-fossil) from 190 published references. Mean (unweighted) δ13C signatures for fossil fuel CH4 are significantly lighter than values commonly used in CH4 budget models, thus highlighting potential underestimation of fossil fuel CH4 emissions in previous CH4 budget models. This living database will be updated every 2–3 years to provide the atmospheric modeling community with the most complete CH4 source signature data possible. Database digital object identifier (DOI): https://doi.org/10.15138/G3201T.
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.
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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.010 |
| 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 it