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Record W2163117943 · doi:10.1002/2015gl063613

Ambient noise tomography across the southern Alaskan Cordillera

2015· article· en· W2163117943 on OpenAlexaboutno aff
Kevin M. Ward

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySeismologyVolcanoSubductionLithosphereCrustAnomaly (physics)Gravity anomalyFault (geology)Seismic tomographyNeogeneTectonicsGeophysicsPaleontologyMantle (geology)

Abstract

fetched live from OpenAlex

Abstract I present the results of an extensive data mining effort integrating 197 permanent and temporary seismic stations into a Rayleigh wave ambient noise study across southern Alaska and westernmost Canada. Principal observations of my tomography model are largely consistent with mapped geology features and previous geophysical studies while providing previously unavailable, laterally continuous details of the southern Alaskan Cordillera lithosphere. At intermediate periods, a geophysically uniform crust is observed north of the Denali Fault and is consistent with a sharp transition in crustal thickness. Under the Wrangell volcanic belt, a prominent low‐phase‐velocity anomaly correlates well with the lateral extent of a relative low‐gravity anomaly and Neogene surface volcanics. At longer periods, a low‐phase‐velocity anomaly bounds the inferred eastern extent of the subducted Yakutat microplate beneath the Wrangell volcanic belt.

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 score0.212
Threshold uncertainty score0.421

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 head0.043
GPT teacher head0.297
Teacher spread0.254 · 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

Citations34
Published2015
Admission routes1
Has abstractyes

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