Biomass with capture: negative emissions within social and environmental constraints: an editorial comment
Bibliographic record
Abstract
Biomass has long been investigated both as a (nearly) CO2 neutral substitute for fossil fuels and as a means for sequestering carbon in terrestrial ecosystems (Kheshgi et al. 2000). More recently, the potential to integrate carbon capture and storage technologies (“CCS”)— conceived to enable fossil fuel use without atmospheric CO2 emissions—with bio-energy systems has emerged as a means to capture atmospheric carbon, fixed through photosynthesis, and sequester it from the atmosphere for geologic timescales (Obersteiner et al. 2001; Yamashita and Barreto 2004; Mollersten et al. 2003; Rhodes and Keith 2005). The ability of such integrated systems to produce energy products with negative net atmospheric carbon emissions could have important implications for mitigating anthropogenic climate change. The scale and timing of biomass-based mitigation is limited by the availability and cost of conversion technologies, many of which are currently inefficient or technologically immature. More fundamentally, it is limited by feasible scales of biomass production, estimates of which are highly uncertain and indicate that the capacities envisioned within aggressive proposals, including those by Read (2008), may not be achievable (Hoogwijk et al. 2003; Berndes et al. 2003). Concern for environmental, social, and economic impacts of biomass development may further constrain production below technically feasible levels. The current biofuels boom may be illustrative in this context. On the one hand, it demonstrates the feasibility of rapid, large-scale bio-energy deployments; while on the other hand, it provides examples of undesirable environmental and social consequences from large-scale biomass production (Ziegler 2007; Rosenthal 2007a, b). Climatic Change (2008) 87:321–328 DOI 10.1007/s10584-007-9387-4
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.048 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".