Imaging of Geologic Samples Using Femtosecond – Laser Desorption Postionization – Mass Spectrometry
Bibliographic record
Abstract
This study uses an MS-imaging method developed by Luke Hanley at the University of Illinois at Chicago, femtosecond – laser desorption post ionization – mass spectrometry (fs-LDPI-MS), which has the ability to make molecular maps of hydrocarbons across the surface and with depth of a sample, to study geologic material for the first time. We used fs-LDPI-MS in tandem with previous geochemical characterization and petrographic analysis to precisely determine the spatial distribution of hydrocarbons at the micron scale within geologic material. We performed analysis on two samples to observe the relationships between biomarkers and their mineral matrix. (1) A 93.5 million year old (Ma) sample which was deposited in the Western Interior Seaway (WIS) and (2) a 2.69 billion year old (Ga) sample from the Abitibi Greenstone Belt in Ontario, Canada. By observing hydrocarbon-host rock relationships, we were able to test hypotheses regarding the timing and mode of indigenous, non-indigenous, and contaminant biomarker emplacement within the samples. We were able to create a depth profile of a suite of contaminants emplaced within the 93.5 Ma sample and also to produce MS-images which display the spatial distribution of isorenieratene derivitives within the sample. We were also able to observe hydrocarbons within the 2.7 Ga samples, but we were unable to definitively locate or precisely date the biomarkers previously observed within the Archean samples.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".