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Record W2008901022 · doi:10.4319/lom.2009.7.411

Profiling neutral lipids in individual fish larvae by using short‐column gas chromatography with flame ionization detection

2009· article· en· W2008901022 on OpenAlexaff
Tara Hooper, Christopher C. Parrish

Bibliographic record

VenueLimnology and Oceanography Methods · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFlame ionization detectorChromatographyGas chromatographyDetection limitChemistryThin-layer chromatographyAnalytical Chemistry (journal)

Abstract

fetched live from OpenAlex

The triacylglycerol (TAG) to sterol (ST) ratio, which has been used to determine the condition index in marine species, has regularly been measured via thin‐layer chromatography with flame ionization detection (TLC/FID) using the Chromarod‐Iatroscan system. However, this method is labor intensive, requires long analysis times, and has a detection limit of ~50 ng. For the determination of lipids in very small samples such as individual fish larvae, short‐column gas chromatography with flame ionization detection (GC/FID) provides an excellent alternative to Iatroscan TLC/FID, owing to its lower detection limit (~1 ng for high‐molecular‐weight TAG and ~0.1 ng for lower‐molecular‐weight species). As well, GC/FID individually profiles lipids based on their carbon number, whereas TLC/FID groups lipids according to their lipid class. Here we describe a method for the determination of neutral lipids from individual Cyclopterus lumpus (lumpfish) and Myoxocephalus scorpius (short‐horn sculpin) larvae using short‐column GC/FID. By using an internal standard and applying weight correction factors, ST and TAG can be accurately and precisely measured. The results reveal that there were no significant differences between GC/FID and TLC/FID in the quantification of TAG or ST in individual fish larvae ( P > 0.05); however, GC/FID is more sensitive, precise, rapid, and cost‐efficient.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.278
Teacher spread0.254 · 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 designObservational
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

Citations8
Published2009
Admission routes1
Has abstractyes

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