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Record W4393656481 · doi:10.5281/zenodo.1215632

Rock-temperature, fracture displacement and acoustic/micro-seismic data measured at Matterhorn Hörnligrat, Switzerland

2018· dataset· en· W4393656481 on OpenAlexaff
Samuel Weber, Jan Beutel, Stephan Gruber, Tonio Gsell, Andreas Hasler, Andreas Vieli

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typedataset
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsCarleton University
Fundersnot available
KeywordsDisplacement (psychology)GeologyFracture (geology)SeismologyAcoustic emissionGeotechnical engineeringMaterials scienceComposite materialPsychology

Abstract

fetched live from OpenAlex

This repository contains data, which were acquired in the context of project X-Sense2 (financed by nano-tera.ch, ref. no. 530659) at the Matterhorn Hörnligrat fieldsite on 3500 m a.s.l. from 2015 until 1 April 2018. These data were used in the following publication: Weber, S., Faillettaz, J., Meyer, M., Beutel, J., and Vieli, A.: Acoustic and micro-seismic characterization in steep bedrock permafrost on Matterhorn (CH), Journal of Geophysical Research: Earth Surface, 123(6), 1363-1385, doi: 10.1029/2018JF004615, 2018. AM-DATA This repository contains selected accelerometer data with SI unit m/s2 (hourly .miniseed-files, MH40 refers to AMscarp). These data were measured continuously using an accelerometer based on a Wilcoxon 728A/T (10 − 10000 Hz, 24 kHz resonance frequency), netADC data acquisition system and netSP+ seismological processor of Institute of Mine Seismology. Data were synchronized to a global time reference using GPS (<1 μs). The data is stored in .miniseed-format and splitted in hourly files. SM-DATA This repository contains selected raw seismometer data in counts (hourly .miniseed-files, MHDL refers to SMscarp and MHDT refers to SMridge). These data were measured using a Lennartz electronic low-noise seismometer LE-3Dlite MKIII (1−100 Hz) and Nanometrics Centaur digital recorder, a 24-bit high-resolution seismic data acquisition system disciplined by GPS (<100 μs) with a sampling rate of 1000 sps. The data is stored in .miniseed-format and splitted in hourly files. TIMESERIES This repository contains 8 timeseries: AS_scarp_high.csv describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R6α, 35−100 kHz, 55 kHz resonance frequency. AS_scarp_low.csv describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R.45, 5−30 kHz, 20 kHz resonance frequency. CR_old.csv described the measured fracture displacement in mm. SMridge_nofilter.csv describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm2/s2. SMridge_filtered.csv describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm2/s2. SMscarp_filtered.csv describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm2/s2. SMscarp_nofilter.csv describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm2/s2. temperature.csv describes the rock temperature (in °C) at different depths: 5, 10, 20, 30, 50 and 100 cm. All time stamps are in UTC.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0310.013

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 head0.026
GPT teacher head0.232
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

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