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Record W4385298267 · doi:10.21203/rs.3.rs-3162070/v1

Weathering of accretionary orogens as a trigger for the Cambrian Explosion

2023· preprint· en· W4385298267 on OpenAlexaff
Mingshuai Zhu, Yinggang Zhang, Daniel Pastor‐Galán, Matthijs Smit, Benjamin Mills, Fuqin Zhang, Carl Guilmette, Laicheng Miao, Shun Li, Ariuntsetseg Ganbat, Shunhu Yang

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversité LavalUniversity of British Columbia
FundersNatural Environment Research CouncilChinese Academy of SciencesMinisterio de Ciencia e InnovaciónNational Natural Science Foundation of China
KeywordsGeologyWeatheringGondwanaEarth sciencePaleontologyPhanerozoicPaleozoicDenudationTectonicsCenozoic

Abstract

fetched live from OpenAlex

Abstract The Cambrian Explosion (540-515 Ma ago) is arguably the most significant evolutionary transition after the origin of life. A variety of environmental perturbations including rising oxygen levels, changes in ocean chemistry and increased bio-essential elements have been correlated to this rapid faunal diversification. Anomalously high weathering fluxes from the continents to the oceans are hypothesized to cause these perturbations, but there is no obvious candidate to produce such enhanced weathering events. The Gondwana’s peripheral orogens and Central Asian Orogenic Belt, provide a unique and unrecognized global accretionary orogen (>18,500 km) concomitant to the Cambrian Explosion, larger than the so-far recognized Cambrian collisional orogens. Our modeling results suggest that the increased weathering of phosphorus from these Cambrian accretionary orogenic belts would stimulate marine primary productivity, increase atmospheric O2, and promote the expansion of shallow-ocean oxygenation at a time coincident with the Cambrian Explosion.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.159
GPT teacher head0.388
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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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