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Record W4393793839 · doi:10.5281/zenodo.6473986

Sask Glacier Dynamic data set

2022· dataset· en· W4393793839 on OpenAlexaboutno aff
Lucas Zoet, Nate Stevens

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierData setSet (abstract data type)GeologyCartographyComputer sciencePhysical geographyGeographyArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

This Sask_Glacier_Dynamics-data.zip file was generated on 2022-April_20 by N Stevens GENERAL INFORMATION 1. Title of Dataset: Sask_Glacier_Dynamics.zip 2. Author Information A. Principal Investigator Contact Information Name: Lucas Zoet Institution: University of Wisconsin - Madison Address: 1215 W. Dayton Street Email: lzoet@wisc.edu B. Corresponding Investigator Contact Information Name: Nathan Stevens Institution: University of Wisconsin - Madison Address: 1215 W. Dayton Street Email: ntstevens@wisc.edu 3. Date of data collection: 1948 to 2019-08 4. Geographic location of data collection: Saskatchewan Glacier, Banff National Park of Canada, Alberta, Canada Madison, Wisconsin All data processing was conducted at the University of Wisconsin - Madison 5. Information about funding sources that supported the collection of the data: N.T.S. was funded by PR-1738913, the Weeks Graduate Fellowship, Department of Geosciences, University of Wisconsin - Madison. N.T.S., C.J.R., D.D.H., and L.K.Z. were funded in part by start-up funds from the Department of Geosciences at the University of Wisconsin - Madison and the Wisconsin Alumni Research Foundation (WARF). R.B.A. and E.S. were funded in part by the Evan Pugh Research Endowment from the Pennsylvania State University. SHARING/ACCESS INFORMATION 1. License/restrictions placed on the data: Citation of paper 2. Links to publications that cite or use the data: None 3. Recommended citation for this dataset: Title: N.T. Stevens, C.J. Roland, L.K. Zoet, R.B. Alley, D.D. Hansen, E. Schwans (2022) Multi-decadal basal slip enhancement at Saskatchewan Glacier, Canadian Rocky Mountains, Journal of Glaciology Authors: N.T. Stevens C.J. Roland L.K. Zoet D.D. Hansen R.B. Alley E. Schwans DATA & FILE OVERVIEW 1. File List: Folders: BOREHOLE - digitized data from the borehole deformation survey in Meier (1957) at Saskatchewan Glacier in .csv format GIS - Geographic Information Systems files ==> GeoRef - GeoTIFF outputs from georeferencing maps from Meier (1957) in *.tif format ==> Moulins - Handheld GPS observations of moulin locations during August 2019 survey in *.txt format ==> Shapefiles - Assorted ESRI shapefiles ==> Meier1957_Extracted - ShapeFiles containing georeferenced point data for survey sites from Meier (1957) ==> Modern_Extracted - ShapeFiles for transect lines used in Stevens and others (2022) ==> Transects - Extracted data along transects from Tennant & Menounos (2013, TM13), Meier (1957, M57), Danielson & Geisch (2011, DG11), and Stevens and others (2022, SGGS) in *.csv format. GPS ==> CUBE - contains DataCube GPS positioin data from the August 2017 survey ==> Meta - Metadata associated with GNSS post processing from the August 2019 survey ==> continuous - contains manually classified data gaps used to create the "stitched" ice-surface displacement record in Stevens and others (2022) in *.txt format ==> postprocessing - contains the RTKLIB postprocessing configuration file and command-line calls for GNSS data from August 2019 ==> POST_PROCESSED - contains post-processed GNSS data from August 2019 ==> campaign - *.csv files for position solutions of GNSS point measurements following post-processing ==> continuous - *.pos (CSV-compliant) file for position solutions of continuous GNSS measurements following post-processing ==> RAW - raw GNSS observations from August 2019 in native formats exported from Emlid antennae ==> BASE - data from GNSS antenna used as the reference station and a python script (*.py) used to estimate the base-station reference location ==> ROVER1 - data from GNSS antenna labeled unit #1 ==> ROVER2 - data from GNSS antenna labeled unit #2 ==> README.md - text file explaining changes in naming convention between acquisition and presentation in Stevens and others (2022) MELT - contains local meteorologic observations and ice-surface ablation measurements ==> LOWER - contains digitized notes on ice-surface ablation in *.csv format from August 2019 ==> WXSG - contains observational data from the automated weather station deployed at Saskatchewan Glacier during August 2019 in *.csv format SEISMIC - contains active-source and passive-source seismic data from August 2017 and 2019 surveys ==> Active - contains median stacked common shotpoint gathers in MiniSEED format and phase arrival time picks in text-format compatable with the Pyrocko Project Snuffler (pyrocko.org) ==> Meta - contains post-processed GNSS surveys of station locations (site.csv) ==> Passive - contains rotated & downsampled passive seismic data and OpenHVSR project used in ice-thickness estimation in Stevens and others (2022) ==> ASCII - contains waveform data in *.txt format compatable with OpenHVSR (Bignardi and others, 2016; 2018) ==> OpenHVSR - contains OpenHVSR projects for the 2017 (OpenHVSR_SS1_proj_100Hz.m) and 2019 (OpenHVSR_proj.m) seismic surveys defined by matlab (*.m) scripts ==> Elaboratioin - elaboration files containing the HV analysis of 2019 survey data ==> SaskSeis_Array - elaboration files containing the HV analysis of 2017 survey data METHODOLOGICAL INFORMATION: For methodological information see N.T. Stevens, C.J. Roland, L.K. Zoet, R.B. Alley, D.D. Hansen, E. Schwans (2022) Multi-decadal basal slip enhancement at Saskatchewan Glacier, Canadian Rocky Mountains, Journal of Glaciology

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)0.001
metaresearch head score (Gemma)0.005
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 score0.936
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0670.075

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 head0.068
GPT teacher head0.262
Teacher spread0.193 · 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
Published2022
Admission routes1
Has abstractyes

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