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Record W2098550800 · doi:10.1371/journal.pone.0139195

Ethanol and High-Value Terpene Co-Production from Lignocellulosic Biomass of Cymbopogon flexuosus and Cymbopogon martinii

2015· article· en· W2098550800 on OpenAlexaff
Blake L. Joyce, Valtcho D. Zheljazkov, Robert W. Sykes, Charles L. Cantrell, Choo Hamilton, David G. J. Mann, Miguel Rodríguez, Jonathan R. Mielenz, Tess Astatkie, C. Neal Stewart

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsDalhousie University
FundersBiological and Environmental ResearchOffice of ScienceOak Ridge National LaboratoryMississippi State UniversityU.S. Department of Energy
KeywordsPanicum virgatumLignocellulosic biomassEthanol fuelBiofuelBiomass (ecology)PanicumChemistryAgronomyBotanyBiologyBioenergyBiotechnology

Abstract

fetched live from OpenAlex

Cymbopogon flexuosus, lemongrass, and C. martinii, palmarosa, are perennial grasses grown to produce essential oils for the fragrance industry. The objectives of this study were (1) to evaluate biomass and oil yields as a function of nitrogen and sulfur fertilization, and (2) to characterize their utility for lignocellulosic ethanol compared to Panicum virgatum (switchgrass). Mean biomass yields were 12.83 Mg lemongrass ha-1 and 15.11 Mg palmarosa ha-1 during the second harvest year resulting in theoretical biofuel yields of 2541 and 2569 L ethanol ha-1 respectively compared to reported 1749-3691 L ethanol ha-1 for switchgrass. Pretreated lemongrass yielded 198 mL ethanol (g biomass)-1 and pretreated palmarosa yielded 170 mL ethanol (g biomass)-1. Additionally, lemongrass yielded 85.7 kg essential oil ha-1 and palmarosa yielded 67.0 kg ha-1 with an estimated value of USD $857 and $1005 ha-1. These data suggest that dual-use crops such as lemongrass and palmarosa may increase the economic viability of lignocellulosic biofuels.

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 score0.002
Threshold uncertainty score0.694

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 head0.038
GPT teacher head0.213
Teacher spread0.175 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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