Synchrotron FTIR investigations of kerogen from Proterozoic organic-walled eukaryotic microfossils.
Bibliographic record
Abstract
Fourier transform infrared spectroscopy (FTIR) provides a rapid non-destructive molecular characterization of organic and inorganic material in geological samples. Combination of qualitative and semi-quantitative approaches are routinely used in FTIR study of kerogen and coals. A diversity of descriptors provides straightforward tools to characterize kerogen type, composition and structure. However, only a few of these descriptors are applied in the chemical investigation of Precambrian organic-walled microfossils. Synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) permits high spatial resolution investigations of organic matter in a large range of applications in biology, geochemistry and cosmochemistry, but remains rarely applied in Precambrian microfossils studies. Here we show that SR-FTIR spectroscopy combined with an integrative approach of kerogen description is particularly relevant for the study of minute organic-walled microfossils of unknown biological origin. The analyses of five morphospecies from three different Proterozoic formations in northwestern Canada highlight kerogen signatures rich in aromatic, aliphatic and oxygenated moieties. This is evidenced by the combined use of spectrum qualitative descriptions (band assignments and positions) and the calculations of semi-quantitative parameters using intensities and integrated areas of absorption bands (CH2/CH3, R3/2, Al/CC, CO/CC, A factor, C factor). Altogether, this study demonstrates the interest of an integrative approach when investigating the chemistry of organic-walled microfossils with FTIR spectroscopy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".