Preliminary characterization of Palaeogene European ambers using ToF‐SIMS
Bibliographic record
Abstract
Amber comprises polymerized plant resins that have a remarkable capacity for survival in a range of geological environments. Most ambers can be traced to coniferous trees and typically combine a broad array of plant natural products including terpenoids, carboxylic acids, and associated alcohols. Because amber may entomb various organisms at the time of production and preserve them with unmatched fidelity, it has been studied for centuries. Despite extensive geochemical profiling of amber‐derived extracts using techniques such as gas‐chromatography mass spectrometry, to date amber compositional variability has not been investigated by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). We conducted a series of scans on microtomed surfaces of Baltic and Bitterfeld ambers, representing two of Europe's major deposits, both of Palaeogene age. We exploited authentic standards of mono‐methyl succinate and diterpene resin acids to guide interpretation of the results. The ToF‐SIMS spectra are highly reproducible for each amber type considered and highlight subtle differences that are likely underscored by differences in age, botanical provenance, and post‐depositional history. Importantly, the abundance of succinate is consistently higher in Baltic amber relative to Bitterfeld amber, suggesting they are distinct deposits and not regional variants of each other. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.002 | 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".