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Record W2584164234 · doi:10.2118/1116-0014-jpt

Guest Editorial: Incentivizing Energy and Climate Innovation

2016· editorial· en· W2584164234 on OpenAlexaboutno aff
Marcius Extavour, Paul Bunje

Bibliographic record

VenueJournal of Petroleum Technology · 2016
Typeeditorial
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)Competitor analysisIncentiveEnergy securityParadigm shiftBusinessEngineeringEconomicsRenewable energyMarketingEcology

Abstract

fetched live from OpenAlex

Guest editorial In a quiet industrial park in suburban Toronto, there is a machine that eats carbon dioxide (CO2) and spits out fuel. This place, typically associated with its strip malls, ethnic and cultural diversity, and peaceful middle class life, might also soon be known as a hotbed of energy innovation. The project’s code name is “Pond.” A world away, at a world-class research institute in Bangalore, India, engineers have developed a completely different technology to convert CO2 into industrial chemicals. They are motivated by the desire to begin tackling rising global CO2 emissions. Their project code name is “Breathe.” Aside from a healthy obsession with carbon, what these two efforts have in common might surprise you. Rather than collaborating on an international science project or with a company’s industrial research and development, both are competing to win a global competition to transform CO2 from a liability into an asset and, in so doing, create a paradigm shift in the energy space. Both are competitors in the NRG COSIA Carbon XPRIZE. And the competition is just getting started. The XPRIZE Foundation relies on the growing power of exponential technologies and revolutionary science to catalyze radical breakthroughs. This means developments in science and technology such as robotics, artificial intelligence, nanotechnology, big data, and other disruptive forces have the potential to show exponential impact on grand challenges such as sustainable energy and climate change. By offering a suite of incentives in a prize competition, XPRIZE seeks to inspire the world’s scientists, technologists, and innovators to tackle seemingly intractable challenges. Transforming our energy systems may be the 21st century’s greatest challenge. Articulating a grand challenge does not solve any problems, but a clear and deep articulation of the problem demands an understanding of the complexity and nuance involved in the problem itself and in a vision for defining characteristics of a solution. In our approach to energy innovation, XPRIZE recognizes that the technological, sociopolitical, and economic changes occurring globally present innovators with a rare opportunity to apply truly groundbreaking research to challenges of worldwide importance. We operate at the intersection of audacious and achievable. The Carbon XPRIZE is a USD 20 million global competition to incentivize technologies that convert CO2 emissions into valuable products. The winning teams will convert the largest quantity of CO2 from actual flue gas from coal or natural gas power plants into one or more products with the highest net value. The 10 teams that survive the first two elimination rounds (proposal evaluation was in summer of 2016, and lab-scale demonstration from late 2016 through 2017) will use two brand new test centers adjacent to operating power plants in western Canada and the US state of Wyoming to demonstrate their solutions at industrial scale.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.272
Teacher spread0.267 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2016
Admission routes1
Has abstractyes

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