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Record W4393417416 · doi:10.47347/incasciences.v1i1.27

Caracterización magnetotelúrica de las fallas y sistema de fallas en el valle del Cusco, sur del Perú

2023· article· es· W4393417416 on OpenAlexaff
Yanet Antayhua, Briant García, Lorena Rossell, Carlos Benavente, Martyn Unsworth

Bibliographic record

VenueIncasciences · 2023
Typearticle
Languagees
FieldEarth and Planetary Sciences
TopicGeological and Tectonic Studies in Latin America
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

En este estudio, se presenta los resultados obtenidos de la investigación magnetotelúrica (MT) llevada a cabo en abril de 2018 en las principales fallas y sistemas de fallas identificados a lo largo del valle del Cusco, sur del Perú. El perfil magnetotelúrico tiende en dirección suroeste-noreste (SO-NE) y consiste de 14 sondeos MT, con espaciamientos de 500 a 700 m. En cada ubicación, se registraron datos de 10 a 24 horas, a fin de medir la resistividad del subsuelo a una profundidad aproximada de 6 km. Estos datos fueron convertidos en un modelo de resistividad eléctrica del subsuelo utilizando un algoritmo de inversión 2-D. El modelo de resistividad eléctrica fue interpretado sobre la base de los estudios geológicos y estructurales. Considerando que las zonas de fallas han mostrado respuestas conductivas en otros estudios, nosotros interpretamos que las zonas conductivas, identificadas en el modelo de resistividad eléctrica, sugieren una significativa correlación con las trazas de las fallas del valle del Cusco. Asimismo, se muestra por primera vez la geometría en profundidad (~6 km) de un sistema extensivo de fallas imbricadas que consisten en fallas normales y activas. Esto también nos invita a considerar a la falla Cusco como una estructura tectónica activa, que podría incrementar el peligro sísmico en el valle del Cusco.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.261
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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