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A non-destructive technique for chemical mapping of insect inclusions in amber

2016· preprint· en· W4232092735 on OpenAlexaff
A. Kolaceke, Ryan C. McKellar, Mauricio Barbi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicFossil Insects in Amber
Canadian institutionsRoyal Saskatchewan MuseumUniversity of Regina
Fundersnot available
KeywordsBaltic amberTaphonomyContext (archaeology)CretaceousSynchrotron radiationPaleontologyGeologyEvolutionary biologyBiologyPhysicsOptics

Abstract

fetched live from OpenAlex

Synchrotron-based techniques offer a wealth of elemental, molecular, and structural insights in biological samples, but the application of these techniques to fossils is a relatively new development. Here we examine how Synchrotron Radiation Micro X-Ray Fluorescence (SR µXRF) provides insights into the chemical composition of insects trapped in amber, while leaving the inclusions unaltered. By analyzing a series of ants (Hymenoptera: Formicidae) that range from modern material, to Eocene Baltic amber, and Late Cretaceous North Carolina amber, we investigate how variable preservation influences the results obtained through SR µXRF analyses, as well as the various merits and pitfalls associated with the application of this technique to amber inclusions. The initial results from this line of research are encouraging. They provide new avenues to study elements that are original to the specimens involved, as well as those generated through decay, or introduced during taphonomic processes. This new technique also suggests a range of complementary techniques that may allow future studies to pursue traces of original colour and cuticular reinforcement in amber inclusions. Ultimately, this work serves as an introduction to the underlying principles, strengths, and limitations associated with applying SR µXRF in a palaeontological context.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.035
GPT teacher head0.264
Teacher spread0.228 · 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 designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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