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Record W1577243332 · doi:10.13020/d6001s

2003 Rock Properties Database: Density, Magnetic Susceptibility, and Natural Remanent Magnetization of Rocks in Minnesota

2003· dataset· en· W1577243332 on OpenAlexaboutno aff
V.W. Chandler, R.S. Lively

Bibliographic record

VenueUniversity of Minnesota Digital Conservancy (University of Minnesota) · 2003
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWarrantDatabaseGeological surveyGeologyScale (ratio)Natural (archaeology)RemanenceMining engineeringComputer scienceMineralogyMagnetizationGeographyGeophysicsBusinessCartographyPhysicsPaleontology

Abstract

fetched live from OpenAlex

Geologic interpretation of gravity and magnetic anomaly data in a given area is greatly enhanced if density, magnetic susceptibility and natural remanent magnetization (NRM) data are available for representative rock-types. Along with outcrop and drill-hole information, rock property data help relate geophysical anomaly signatures to probable rock types, and provide constraints on the use of anomaly data as a tool for mapping and for modeling geology at depth. Most of the density and magnetization data contained in this database were acquired over the last two decades by the Minnesota Geological Survey (MGS) as part of an on-going program to collect rock properties. A group of Paleozoic samples were collected from Iowa and included in the database because they provide a representative suite of data for rocks present, but not widely exposed in Minnesota. Additional data were derived from studies by the U. S. Geological Survey (Bath, 1962; Beck, 1970; Beck and Lindsley, 1969; Books, 1972; Jahren, 1965), The University of Minnesota (Bleifuss, 1952, Mooney and Bleifuss, 1952), The University of Western Ontario (Palmer, 1970), and the Geological Survey of Canada (Dubois, 1962).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.180
Teacher spread0.168 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2003
Admission routes1
Has abstractyes

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