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Record W3121125205 · doi:10.1002/9781119507444.ch20

Large Igneous Provinces (LIPs) and Anoxia Events in “The Boring Billion”

2021· other· en· W3121125205 on OpenAlexaff
Shuan‐Hong Zhang, Richard E. Ernst, Junling Pei, Yue Zhao, Guohui Hu

Bibliographic record

VenueGeophysical monograph · 2021
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeologyPaleontologyProterozoicCratonNatural (archaeology)Sedimentary rockEarth scienceTectonics

Abstract

fetched live from OpenAlex

Recent results on ca. 1,380 Ma LIPs and black shales indicate a temporal link between LIPs and black shales and suggest a potential way for using coeval LIPs and black shales as natural markers for boundaries in the Mesoproterozoic timescale. In this chapter, we provide a brief review of LIPs and black shales during “the Boring Billion” in different cratons. The results show that in addition to the ca. 1,380 Ma LIP activity that is coeval with black shales, other major LIPs during this period can preliminarily be divided into several stages and are likely/possibly contemporaneous with black shales, especially the 1,650–1,620 Ma and ca. 1,100 Ma LIPs. Our results demonstrate that the global-scale LIPs and black shales in the Boring Billion can potentially be used as natural markers for subdivisions of the Proterozoic timescale. The results also suggest that, while the Boring Billion was characterized by suboxic or mildly oxygenated marine basins, it was interrupted by several OAEs that are partly caused by the environmental impact of LIPs. Preliminarily proposed correlation of LIPs and black shales provide a new way to explain the fluctuating evolution of atmosphere, life, and marine basins during the Boring Billion as identified by many recent studies.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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 designTheoretical or conceptual
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

Citations13
Published2021
Admission routes1
Has abstractyes

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