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Record W2049879126 · doi:10.2118/2008-020

Carbon Harvesting for Saving the Planet

2008· article· en· W2049879126 on OpenAlexaff
Suraj Gupta

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPlanetAstrobiologyCarbon fibersEnvironmental scienceComputer scienceAstronomyPhysics

Abstract

fetched live from OpenAlex

Abstract Business as usual with consumption of fossil energy will see rapidly rising atmospheric CO2 concentration in the coming decades, unless radical measures are taken for capture and sequestration of carbon. Of the various approaches known to accomplish this, a closer examination of the two prominent methods of sequestration, namely, geo-sequestration and biosequestration and their large scale implementation will be necessary. As previously shown, preservation of the produced biomass (via conversion to charcoal) is a critical step in making the biosequestration work. The benefits of a two step charcoal approach, unparalleled by other methods, include permanence of sequestration, ready verification, the global applicability of the method, and creation of an energy bank for the future generations. However, two apparent issues with this approach are the associated length of time and land requirements. In this paper a couple of ways to solve the land requirement issue are discussed. It is shown that by tapping into the natural cycle of biomass production-decomposition to procure biomass for charcoal sequestration (an approach named as DUCS) eliminates any significant land requirement. According to this method, selective and intelligent use of the annual terrestrial litter fall will not only help accelerate the process of sequestration and obviate the land requirement but also have the overarching benefit of reducing overall sequestration costs. This approach is compared with biofuels use (also aimed at GHG reduction) both in terms of effectiveness and potential cost. Furthermore a practical way forward to initiate implementation of DUCS is discussed. Introduction Excess CO2 emitted into the atmosphere on account of continued consumption of fossil fuels can be offset by capturing it at industrial sources and pumping it into deep geological formations1 or by growth of biomass2 employing naturally occurring photosynthesis. Biomass in this context refers to non-fossil organic materials such as wood, straw, vegetable oils, and biodegradable wastes from plants or animals and agricultural residues that could be used for energy generation. It also includes aquatic living, or recently dead organic material such as phytoplankton or algae. While the technology for CO2 capture and sequestration in eological formations (geo-sequestration) exists, the main drawback of this approach is the associated high cost3 and the fact that it is practical only in the vicinity of concentrated sources of CO2, such as large industrial complexes and power generation plants. Additionally, this method requires raising CO2 to higher pressures which, being an energy intensive process, generates even more CO2. Natural production of biomass on earth employs sun and absorbs CO2 from atmosphere and sequesters it in the form of polysaccharides (bio-sequestration). So in principal by growing more and more plant life, progressively greater amount of carbon can be sequestered. The issue with this approach is that the sequestered biomass eventually decomposes and its decay releases back to atmosphere the sequestered CO24, 5, 6, 7. Thus the carbon is sequestered in the biomass only for the duration between its creation and decay, termed as bio-storage period in [4].

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.006

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.

Opus teacher head0.031
GPT teacher head0.224
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

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