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Record W2073966342 · doi:10.5376/ijms.2013.03.0005

Biodiesel Fuel Production from Marine Microalgae <i>Isochrysis galbana</i>, <i>Pavlova lutheri</i>, <i>Dunaliella salina</i> and Measurement of its Viscosity and Density

2013· article· en· W2073966342 on OpenAlex
Sujin Jeba Kumar T.

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Marine Science · 2013
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDunaliella salinaIsochrysis galbanaBiodieselBiodiesel productionDunaliellaBiofuelFood scienceViscosityBotanyBiologyChemistryAlgaeBiotechnologyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Biodiesel is a fuel derives from transesterification of fats and oils. It is renewable and non-toxic ecofriendly fuel with less CO 2 and NO 2 emissions. Microalgae are known to contain more lipid content than macroalgae and most other oil crops. In this study, we extracted biodiesel from three microalgae Isochrysis galbana , Pavlova lutheri , Dunaliella salina and also measured the density and viscosity of biofuel obtained from these microalgae. Pavlova lutheri yielded more oil than the other two algae with biomass left over Dunaliella salina was more. The density of biodiesel obtained from these microalgae was between 0.86 g/cm 3  and 0.90 g/cm 3 with viscosity in the range 3.92 mm 2 /sec to 4.5 mm 2 /sec showing high density than the other oils.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.224
Teacher spread0.209 · 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