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Record W4414559428 · doi:10.1111/ter.70016

Cryogenian Glacial Erosion and Tectonics as Agents of Crustal Recycling

2025· article· en· W4414559428 on OpenAlexaff
Marina Seraine, Christopher J. Spencer, Thomas Gernon, Thea Hincks, Christopher L. Kirkland, Elias J. Rugen, Leandro G. DaSilva, Hadi Shafaii Moghadam, Luana Pádua Soares, Gabriella Fazio

Bibliographic record

VenueTerra Nova · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsQueen's UniversityGeological Survey of Canada
Fundersnot available
KeywordsZirconRodiniaGlacial periodSupercontinentCrustSubductionBreakupLarge igneous province

Abstract

fetched live from OpenAlex

ABSTRACT Zircon preserves evidence of recycling processes that link surface environments to the mantle. Combined δ 18 O‐εHf in zircon fingerprints magmatic sources and tracks how crustal material is reworked over time. We apply statistical analyses to a global compilation that apparently resolves shifts in zircon U–Pb, δ 18 O, and Lu‐Hf data spanning the Neoproterozoic. Between ~750 and 705 Ma, a decline in crustal residence ages suggests recycling of juvenile crust into subduction zones, overlapping with the onset of the Sturtian glaciation and potentially driven by erosion of Tonian basaltic provinces. After 705 Ma, residence ages increase, marking intensified crustal recycling during the Sturtian and Marinoan glaciations, supported by εHf and δ 18 O change points at ~690 Ma. This transition towards greater incorporation of ancient sediments may reflect tectonic instability during Rodinia's breakup and glacial erosion. These findings suggest a complex interplay between tectonics, climate, and large igneous province processes in shaping Earth's crustal evolution.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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