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Record W4389946000 · doi:10.2138/gselements.19.5.276

How Large Igneous Provinces Have Killed Most Life on Earth—Numerous Times

2023· article· en· W4389946000 on OpenAlexaff
Stephen E. Grasby, David P.G. Bond

Bibliographic record

VenueElements · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsExtinction eventBiosphereEarth scienceLarge igneous provinceAtmosphere (unit)GeologyBiogeochemical cycleAstrobiologyPhanerozoicOzone layerPaleozoicAbiogenesisPaleontologyPermian–Triassic extinction eventLate Devonian extinctionTerrestrial ecosystemPermianEcosystemEcologyGeographyTectonicsClimatologyMagmatismMeteorologyBiology

Abstract

fetched live from OpenAlex

Evolution has not been a simple path. Since the first appearance of complex life, there have been several mass extinctions on Earth. This was exemplified by the most severe event during the Phanerozoic, the end-Permian mass extinction that occurred 252 million years ago and saw a loss of 90% and 70% of all marine and terrestrial species, respectively. Such mass extinctions have entirely reset ecosystems. Increasing evidence points to the massive eruption and crustal emplacement of magmas associated with large igneous provinces (LIPs) as key drivers of these events. Understanding how LIP events disrupted global biogeochemical cycles is of prime importance, especially as humans alter the atmosphere and biosphere today. We explore the cascading impacts of LIP events on global climate, oceans, and land—including runaway greenhouses, the release of toxic metals to the environment, the destruction of the ozone layer, and how global oceans are driven to anoxic and acidic states—all of which have parallels in the consequences of modern industrialisation.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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

Citations38
Published2023
Admission routes1
Has abstractyes

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