Rapid Screening of Glycerol Ether Lipid Biomarkers in Recent Marine Sediment Using Atmospheric Pressure Photoionization in Positive Mode Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Many of the molecular proxies commonly used for paleoenvironmental reconstruction are focused on a limited set of glycerol ether lipids, mainly due to the lack of more comprehensive analytical methods and instrumentation able to deal with a more diverse range of species. In this study, we describe an FTICR-MS-based method for rapid, nontargeted screening of ether lipid biomarkers in recent marine sediments. This method involves simplified sample preparation and enables rapid identification of known and novel ether lipid species. Using this method, we were able to identify complete series of core glycerol dialkyl glycerol tetraethers (GDGTs with 0 to 8 alicyclic rings), including the complete resolution of GDGT-4 and the unexpected detection of GDGTs with more than 5 rings, in sediments from mesophilic marine environments (sea surface temperature, SST, of 24-25 °C). Additionally, mono- and dihydroxy-GDGT analogs (including novel species with >2 rings), as well as glycerol dialkanol diethers, GDDs (including novel species with >5 rings) were detected. Finally, we putatively identified other, previously unreported groups of glycerol ether lipid species. Adequacy of the APPI-P FTICR-MS data for the determination of commonly used GDGT-based proxy indices was demonstrated. The results of this study show great potential for the use of FTICR-MS as both a rapid method for determining existing proxy indices and, perhaps more importantly, as a tool for the early detection of possible new biomarkers and proxies that may establish novel geochemical relationships between archaeal ether lipids and key environmental-, energy-, and climate-related system variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".