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Record W2126365637 · doi:10.1093/ijlct/ctr017

Algal biodiesel production from power plant exhaust and its potential to replace petrodiesel and reduce greenhouse gas emissions

2011· article· en· W2126365637 on OpenAlex
K. Hundt, Bale V. Reddy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Low-Carbon Technologies · 2011
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBiodieselDiesel fuelBiofuelCommercializationGreenhouse gasEnvironmental scienceAlgae fuelWaste managementFossil fuelEngineeringBusinessEcologyChemistryBiology

Abstract

fetched live from OpenAlex

The production of biofuels and other products from algae is a technology that is rapidly developing. This paper presents an overview of algae, its benefits over other biofuel sources and the technology involved in producing algal biofuel. The case study in this report looks at the potential of algal biodiesel, produced using power plant exhaust, to replace our current petrodiesel supply and consequently reduce greenhouse gas emissions. The results suggest that using 60% of all coal and gas power plants would allow this new fuel source to replace petrodiesel entirely and thus reduce greenhouse gas emissions by ∼5%. The challenge at the present is to improve the efficiency of algal fuel production technology so as to lower the cost of algal biodiesel and thereby make it commercially competitive with petrodiesel. Researchers are currently developing various means of accomplishing this and successful commercialization is anticipated by 2018.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.239
Teacher spread0.219 · 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