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Record W2090574238 · doi:10.3109/07388551.2012.695333

Engineering challenges in biodiesel production from microalgae

2012· review· en· W2090574238 on OpenAlex

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

VenueCritical Reviews in Biotechnology · 2012
Typereview
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsWestern University
Fundersnot available
KeywordsBiodieselBiodiesel productionFossil fuelBiofuelRenewable energyEnvironmental scienceBiochemical engineeringRenewable fuelsGreenhouse gasCombustionWaste managementBiotechnologyPulp and paper industryChemistryEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

In recent years, the not too distant exhaustion of fossil fuels is becoming apparent. Apart from this, the combustion of fossil fuels leads to environmental concerns, the emission of greenhouse gases and issues with global warming and health problems. Production of biodiesel from microalgae may represent an attractive solution to the above mentioned problems, and can offer a renewable source of fuel with fewer pollutants. This review presents a compilation of engineering challenges related to microalgae as a source of biodiesel. Advantages and current limitations for biodiesel production are discussed; some aspects of algae cells biology, with emphasis on cell wall composition, as it represents a barrier for fatty acid extraction and lipid droplets are also presented. In addition, recent advances in the different stages of the manufacturing process are included, starting from the strain selection and finishing in the processing of fatty acids into biodiesel.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.129
GPT teacher head0.340
Teacher spread0.210 · 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