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Record W4376271949 · doi:10.21203/rs.3.rs-2875316/v1

Detection of primary paleomagnetic remanence carriers in zircon crystals using high-resolution X-ray ptycho-tomography

2023· preprint· en· W4376271949 on OpenAlexaff
Venkata Sree Charan Kupp, Matthew Ball, Darren Batey, Kathryn Dodds, Silvia Cipiccia, Kaz Wanelik, Roger Fu, Christoph Rau, R. J. Harrison

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsCanadian Light Source (Canada)
FundersDiamond Light Source
KeywordsRemanenceZirconMagnetiteTomographyMaterials scienceResolution (logic)PaleomagnetismGeologyMagnetic fieldMineralogyOpticsMagnetizationPhysicsGeochemistryGeophysicsPaleontology

Abstract

fetched live from OpenAlex

Abstract We present the first application of X-ray ptycho-tomography to perform high-resolution, non-destructive, three-dimensional (3D) imaging of magnetic inclusions in paleomagnetically relevant materials (zircon crystals from the Bishop Tuff ignimbrite). Correlative microscopy using quantum diamond magnetic microscopy combined with X-ray fluorescence mapping was used to locate regions containing Fe-bearing magnetic remanence carriers. Ptycho-tomographic reconstructions with voxel sizes 85 nm and 21 nm were achievable across a field-of-view > 80 µm; voxel sizes as small as 5 nm were achievable over a limited field-of-view using local ptycho-tomography. Magnetite particles 300 nm in size were clearly resolved. We estimate that particles as small as 100 nm – approaching single-domain threshold for magnetite – could be resolvable using this “dual-mode” methodology. Our combined magnetic and tomography results support the presence of primary magnetic inclusions in relatively young and pristine zircon crystals, majority of which are in the optimal size range for carrying strong and stable paleomagnetic signals.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.363
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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