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Record W3006375384 · doi:10.34194/geusb.v35.4398

Colophon, contents, preface

2016· article· en· W3006375384 on OpenAlexaboutno aff
Flemming G. Christiansen

Bibliographic record

VenueGeological Survey of Denmark and Greenland Bulletin · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyGeological surveyMarine geologyTectonicsEarth scienceBoreholePaleontologyGeochemistryOceanography

Abstract

fetched live from OpenAlex

This issue of​ Review of Survey Activities presents a selection of 24 papers reflecting the wide spectrum of current activities of the Geological Survey of Denmark and Greenland, from the microscopic to the plate-tectonic level. The Survey’s activities in Denmark are illustrated by 11 papers covering widely different subjects including groundwater management, pesticide monitoring, 3D urban geology, the regional Danish potential for geothermal energy, Palaeozoic stratigraphy from borehole logs, enhancement of oil production by injection of ‘smart’ water and glacial geology. Activities in Greenland are illustrated by eight papers on Precambrian crustal evolution and mineralisation processes in South-East and northern West Greenland, fundamental magma processes in the Skaergaard intrusion, onshore and offshore seismological studies, and on long-term monitoring of the Greenland ice sheet and sea-ice variability.
 International studies by the Survey are represented by two papers describing a large Nordic CO2-storage project and a pilot study of burial and exhumation along the eastern passive margin of Labrador and Newfoundland using apatite fission track analysis. Finally, three papers describe new developments in the digital access to, and handling of Greenland-related geodata and presentation of a new smartphone- and tablet-based app for effective handling of geological and sample data during field work.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.032
GPT teacher head0.212
Teacher spread0.180 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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