Bibliographic record
Abstract
Covering only 3% of the land area, northern peatlands store about 30% of the global soil carbon and account for 5 to 10% of the global methane burden to the atmosphere. A review of the literature on net ecosystem exchange, net primary productivity, carbon mineralization, methane emissions, and dissolved organic carbon dynamics indicates that peatlands can be both C sources and sinks. The temporal and spatial variability of fluxes is large, but a substantial portion of this variation can be explained by environmental and ecological variables. Uncertainty in predictions about carbon dynamics under changing environmental conditions arises from a number of knowledge gaps: (i) the understanding of how organic matter is mineralized and partitioned into carbon dioxide, methane, and dissolved organic carbon is insufficient; (ii) little is known about the consequences of long-term and short-term disturbances, such as elevated carbon dioxide concentrations, nitrogen and sulfur deposition, fire, and droughts, on the individual components of the carbon cycle; (iii) models that capture the dynamic interaction of the processes and their controls have not been developed yet, with the notable exception of methane dynamics. Key words: peatlands, carbon cycle, organic matter, carbon dioxide, methane, climate change.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".