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Record W3106103009 · doi:10.3390/en13225960

Evaluating the Potential of Rhodosporidium toruloides-1588 for High Lipid Production Using Undetoxified Wood Hydrolysate as a Carbon Source

2020· article· en· W3106103009 on OpenAlexaff
Rahul Saini, Krishnamoorthy Hegde, Carlos S. Osorio‐González, Satinder Kaur Brar, Pierre Vezina

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsGDG EnvironnementYork University
Fundersnot available
KeywordsHydrolysateChemistryBiomass (ecology)HardwoodBiofuelFood scienceLignocellulosic biomassSugarRaw materialBioenergyPulp and paper industryBotanyBiologyBiochemistryOrganic chemistryAgronomyBiotechnologyHydrolysisFermentation

Abstract

fetched live from OpenAlex

The study aims to explore microbial lipid production using an abundant and low-cost lignocellulosic biomass derived from forestry residues. Sugar-rich undetoxified hydrolysate was prepared using hardwood and softwood sawdust and used for lipid production as a carbon source from an oleaginous yeast, Rhodosporidium toruloides-1588. The maximum biomass obtained was 17.09 and 19.56 g/L in hardwood and softwood hydrolysate, respectively. Sugar consumption in both hydrolysates was >95%, with a maximum lipid accumulation of 36.68% at 104 h and 35.24% at 96 h. Moreover, R. toruloides-1588 exhibited tolerance to several toxic compounds such as phenols, organic acids and furans present in hydrolysates. The lipid characterization showed several monosaturated and polyunsaturated fatty acids, making it a potential feedstock for biofuels and oleochemicals production. This study confirms the credibility of R. toruloides-1588 as a suitable lipid producer using hydrolysates from forestry residues as a substrate. Additionally, lipids obtained from R. toruloides-1588 could be a potential feedstock for advanced biofuel production as well as for food and pharmaceutical applications.

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 score0.188
Threshold uncertainty score0.399

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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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