An Initial Evaluation of Some Biogeochemical-engineering Routes to Carbon Management
Bibliographic record
Abstract
Popular carbon management strategies focus in part, on removal of CO2 from flue gas streams or even from the atmosphere to subsurface storage. Alternative approaches to climate change mitigation, which effectively convert degradable biomass to inert carbon, radiate the sun’s energy back into space or sequester CO2 into solid form, may also have merit. In this paper we present initial scoping studies for three possible alternative biogeochemical-engineering carbon management schemes suggested by observations from our studies of petroleum systems and fossil fuel energy recovery systems. We evaluate the feasibility of recovering hydrogen, instead of oil, directly from oilfields undergoing natural biodegradation processes and we also examine the feasibility of using a related process, biologically assisted carbon capture and conversion of CO2 to methane, via H2 + CO2 methanogenesis in the hydrogen-rich environments of weathering subsurface ultrabasic rocks, as a route to recycle carbon dioxide in flue gases as methane. We also cautiously examine the use of restricted portions of forests to produce, at very large scale, functionalized, biologically refractory water soluble carbon compounds, similar to oceanic dissolved organic matter, that would survive as stable inert carbon sequestration materials. We look at some of the engineering, energy and geochemical barriers to the feasibility of these possible technology routes. While rapid, substantial carbon emissions reductions are by far the desired course of action, we feel prudent scoping of potential alternative routes, some of which will inevitably be risky is however desirable.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".