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Record W4388758335 · doi:10.1130/g51800.1

Strongly peraluminous granites provide independent evidence for an increase in biomass burial across the Precambrian–Phanerozoic boundary

2023· article· en· W4388758335 on OpenAlexaff
Sami Mikhail, Eva E. Stüeken, Toby Boocock, Megan Athey, Nick Mappin, Adrian J. Boyce, Janne Liebmann, Christopher J. Spencer, Claire E. Bucholz

Bibliographic record

VenueGeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsQueen's University
FundersNatural Environment Research CouncilSight Research UK
KeywordsPhanerozoicPrecambrianProterozoicGeologyArcheanPaleontologySiliciclasticAuthigenicSedimentary rockEarth scienceGeochemistrySedimentary depositional environmentCenozoic

Abstract

fetched live from OpenAlex

Abstract Strongly peraluminous granites (SPGs) are generated by the partial melting of sedimentary rocks and can thus provide a novel archive to reveal secular trends in Earth’s environmental history that integrate siliciclastic sedimentary lithologies. The nitrogen (N) content of Archean, Proterozoic, and Phanerozoic SPGs reveals a systematic increase across the Precambrian–Phanerozoic boundary. This rise is supported by a coeval increase in the phosphorus (P) contents of SPGs. Collectively, these data are most parsimoniously explained by an absolute increase in biomass burial in the late Proterozoic or early Phanerozoic by a factor of ~5 and as much as 8. The Precambrian–Phanerozoic transition was a time of progressive oxygenation of surface environments paired with major biological innovations, including the rise of eukaryotic algae to ecological dominance. Because oxygenation suppresses biomass preservation in sediments, the increase in net biomass burial preserved in SPGs reveals an expansion of the biosphere and an increase in primary production across this interval.

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)0.001
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.303
Teacher spread0.254 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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