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Record W3134820709 · doi:10.1111/ter.12524

The effective elastic thickness of the lithosphere in the Amerasia Basin, Arctic Ocean

2021· article· en· W3134820709 on OpenAlexaboutno aff
Zilong Ling, Lihong Zhao, Tao Zhang, Guo‐Jun Zhai, Fanlin Yang

Bibliographic record

VenueTerra Nova · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLithosphereGeologyStructural basinArcticTectonicsMorlet waveletThe arcticSeismologyGeomorphologyWaveletOceanographyWavelet transform

Abstract

fetched live from OpenAlex

Abstract As a proxy for the lithospheric strength at the time of loading, the effective elastic thickness of the lithosphere (Te) can aid in understanding the structure and evolution of the lithosphere. Here, we obtain spatial variations in Te of Amerasia Basin and surrounding regions using the fan‐shaped Morlet wavelet method. Our results show that variations in Te generally agree well with tectonic provinces in the region. The eastern part of Makarov Basin has the same lithospheric strength as Alpha and Mendeleev ridges, which may be attributed to the influence of the High Arctic large igneous province. N‐S and E‐W Te variations in Podvodnikov Basin may reflect two phases of its formation. Western and eastern parts of south Canada Basin have prominent low and high Te values, respectively. This study provides the reliable Te results of central Arctic and improves our understanding of the evolution of the Arctic.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

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)0.0010.000

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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