Preface: Towards a full greenhouse gas balance of the biosphere
Bibliographic record
Abstract
Ecosystem greenhouse gas (GHG) emissions (CO2, CH4, and N2O) represent a major driver of global environmental change (IPCC, 2014). While there exists an emerging understanding on the net exchange of CO2 across terrestrial and aquatic ecosystems due in part to the existence of large measurement and modeling networks (Baldocchi et al., 2001; Friend et al., 2007; Raymond et al., 2013; Tranvik et al., 2009), similar information on the biosphere–atmosphere exchange of non-CO2 greenhouse gases (i.e., CH4 and N2O) is sparsely available in comparison. To date, a strong focus has been given to so-called high-emission ecosystems, such as wetlands, rivers and lakes, rice cultivations and ruminants for CH4 (Nisbet et al., 2014) and agricultural ecosystems for N2O emissions (Butterbach-Bahl et al., 2013; Sutton et al., 2007). Even though CO2, CH4, and N2O emissions have been increasing during the last century, a combined quantification of the exchange of these three major greenhouse gases from a wide range of terrestrial and aquatic ecosystems is still missing. Therefore, approaches to develop full greenhouse gas monitoring networks, as currently undertaken in larger environmental research infrastructures such as ICOS (Integrated Carbon Observation System) and NEON (National Ecological Observation Network), are highly valuable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.039 |
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".