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Record W6910783335 · doi:10.5066/p93sy9li

Airborne electromagnetic and magnetic survey data, northeast Wisconsin (ver. 1.1, June 2022)

2022· dataset· en· W6910783335 on OpenAlexaboutno aff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBedrockTerrainGeological surveyGeophysical surveyMagnetic surveyProspectingAeromagnetic surveyElectrical resistivity tomography

Abstract

fetched live from OpenAlex

Airborne electromagnetic (AEM) and magnetic survey data were collected during January and February 2021 over a distance of 3,170 line kilometers in northeast Wisconsin. These data were collected in support of an effort to improve estimates of depth to bedrock through a collaborative project between the U.S. Geological Survey (USGS), Wisconsin Department of Agriculture, Trade, and Consumer Protection (DATCP), and Wisconsin Geological and Natural History Survey (WGNHS). Data were acquired by SkyTEM Canada Inc. with the SkyTEM 304M time-domain helicopter-borne electromagnetic system together with a Geometrics G822A cesium vapor magnetometer. The survey was acquired at a nominal flight height of 30 - 40 m above terrain along parallel flight lines oriented northwest-southeast with nominal line spacing of 0.5 miles (800 m). AEM data were inverted to produce models of electrical resistivity along flight paths, with typical depth of investigation up to about 300 m and 1 - 2 m near-surface resolution. Shallow resistivity transitions were used to estimate depth to bedrock across the survey area.

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)0.004
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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.1130.006

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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 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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