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Record W3177966624 · doi:10.1038/s41467-021-24419-8

Fingerprinting the Cretaceous-Paleogene boundary impact with Zn isotopes

2021· article· en· W3177966624 on OpenAlexaff
Ryan Mathur, Brandon Mahan, Marissa Spencer, Linda Godfrey, Neil H. Landman, Matthew P. Garb, D. Graham Pearson, Sheng‐Ao Liu, Francisca E. Oboh‐Ikuenobe

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Alberta
FundersNational Science Foundation
KeywordsGeologySedimentary rockIsotopeWeatheringIsotopic signatureGeochemistryDiagenesisMeteoriteIsotopes of oxygenCarbonateEjectaMineralogyChemistryAstrobiology

Abstract

fetched live from OpenAlex

Numerous geochemical anomalies exist at the K-Pg boundary that indicate the addition of extraterrestrial materials; however, none fingerprint volatilization, a key process that occurs during large bolide impacts. Stable Zn isotopes are an exceptional indicator of volatility-related processes, where partial vaporization of Zn leaves the residuum enriched in its heavy isotopes. Here, we present Zn isotope data for sedimentary rock layers of the K-Pg boundary, which display heavier Zn isotope compositions and lower Zn concentrations relative to surrounding sedimentary rocks, the carbonate platform at the impact site, and most carbonaceous chondrites. Neither volcanic events nor secondary alteration during weathering and diagenesis can explain the Zn concentration and isotope signatures present. The systematically higher Zn isotope values within the boundary layer sediments provide an isotopic fingerprint of partially evaporated material within the K-Pg boundary layer, thus earmarking Zn volatilization during impact and subsequent ejecta transport associated with an impact at the K-Pg.

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: Observational · 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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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