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Record W4386614966 · doi:10.7717/peerj-achem.27

Molecular species composition of polar lipids from two microalgae <i>Nitzschia palea</i> and <i>Scenedesmus costatus</i> using HPLC-ESI-MS/MS

2023· article· en· W4386614966 on OpenAlexaff
Nicolás Mazzella, Mariem Fadhlaoui, Aurélie Moreira, Soizic Morin

Bibliographic record

VenuePeerJ Analytical Chemistry · 2023
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAgence Nationale de la Recherche
KeywordsGalactolipidsGalactolipidChemistryLipidomicsFatty acidDegree of unsaturationPolyunsaturated fatty acidChromatographyGlycolipidAlgaeBiochemistryBiologyBotanyPhospholipidMembraneChloroplast

Abstract

fetched live from OpenAlex

This study examines the polar lipid profiles of two freshwater algae, Scenedesmus costatus and Nitzschia palea . HILIC-ESI-MS/MS analysis was used to determine and quantify the major phospholipids and glycolipids, as well as their relative molecular species, extracted from the two microalgal cultures. Glycolipids were eluted first, followed by phospholipids partially co-eluting with a sulfoglycolipid. The fragmentation pattern in the negative ionization mode for galactolipids was studied, revealing the stereospecific distribution of fatty acids on the glycerol backbone. Green algae frequently include 18:3 fatty acid in both phospholipids and galactolipids, while monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) were more saturated and contained shorter acyls. The diatom phospholipids contained mainly molecular species with saturated or monounsaturated fatty acids, while MGDG and DGDG exhibited a higher proportion of polyunsaturated fatty acids, such as the unique and abundant MGDG (20:5/20:2).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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