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Record W3027838353 · doi:10.1139/er-2020-0004

Utilization of lipid-extracted biomass (LEB) to improve the economic feasibility of biodiesel production from green microalgae

2020· article· en· W3027838353 on OpenAlexaffvenue
Sabrina M. Desjardins, Corey A. Laamanen, Nathan Basiliko, John A. Scott

Bibliographic record

VenueEnvironmental Reviews · 2020
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBiodieselBiomass (ecology)BiofuelRaw materialBiodiesel productionBioenergyPulp and paper industryEnvironmental scienceDiesel fuelPolyunsaturated fatty acidCarbon dioxideFood scienceBiotechnologyChemistryWaste managementBiologyFatty acidAgronomyEcologyBiochemistryEngineering

Abstract

fetched live from OpenAlex

Photosynthetic green microalgae are eukaryotic microorganisms that can mitigate anthropogenic carbon dioxide and generate lipids as a feedstock for production of biodiesel. Biodiesel production may not, however, compete economically with fossil fuel sourced diesel, but obtaining additional value from the biomass left after lipid extraction has the potential to help make the overall process more cost-effective. This review focuses on these additional value-added options that obtain and utilize either whole lipid-extracted biomass (LEB), which typically constitutes 60%–70% of total cell mass, or specific non-biodiesel lipid components such as polyunsaturated fatty acids, carbohydrates, and proteins.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.215
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.050
GPT teacher head0.264
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2020
Admission routes2
Has abstractyes

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