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Record W4375853840 · doi:10.1130/g51037.1

Collapse of terrestrial ecosystems linked to heavy metal poisoning during the Toarcian oceanic anoxic event

2023· article· en· W4375853840 on OpenAlexaff
Viktória Baranyi, Xin Jin, Jacopo Dal Corso, Zhiqiang Shi, Stephen E. Grasby, David B. Kemp

Bibliographic record

VenueGeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsEcological successionAnoxic watersTerrestrial ecosystemEcosystemGeologyTerrestrial plantVegetation (pathology)Earth sciencePalynologyEcologyEnvironmental scienceOceanographyPollenBiology

Abstract

fetched live from OpenAlex

Abstract The Early Jurassic Toarcian oceanic anoxic event (T-OAE, ca. 183 Ma) was accompanied by a major biotic turnover in the oceans and substantial vegetation change on land. The marine biotic crisis has been attributed to several triggers, e.g., anoxia, warming, ocean acidification, yet the processes underlying the collapse of the terrestrial ecosystem are poorly understood. New high-resolution geochemical and palynological data across the T-OAE from a lacustrine succession in North China reveal elevated occurrences of spore dwarfism, asymmetrical Classopollis tetrads, and aberrant spores coeval with increases in heavy metal (Hg, Cu, Cr, Cd, Pb, As) abundances. The occurrence of teratological spores and pollen in multiple plant groups suggests overall vegetation-scale ecological pressure. Our data indicate that the combination of a widespread floral crisis with higher terrestrial organic matter oxidation and decomposition, enhanced hydrological cycle, and coeval large-scale volcanism resulted in higher concentrations of toxic heavy metals in terrestrial ecosystems. These heavy metals could poison plants, causing mutations and disrupting their reproductive cycle, and making them more vulnerable to secondary stresses such as climatic extremes and/or habitat shifts, eventually leading to widespread collapse across all terrestrial trophic levels.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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 teacher head, 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

Citations24
Published2023
Admission routes1
Has abstractyes

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