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Remote predictive geological mapping as a tool for the reconstruction of the complex geodynamic evolution of Melanesia

2020· article· en· W3119739859 on OpenAlexaff
Philipp A. Brandl, Anna Kraetschell, Justin Emberley, Mark D. Hannington, Margaret Stewart, Sven Petersen, Alan Baxter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsMount Royal UniversityUniversity of Ottawa
Fundersnot available
KeywordsGeologySubmarine pipelineGeologic mapSubductionStructural basinTectonicsDrillingScale (ratio)Geological surveyPaleontologySeismologyOceanographyCartographyGeography

Abstract

fetched live from OpenAlex

<p>The offshore regions of Eastern Papua New Guinea and the Solomon Islands include several active and remnant arc and backarc systems that formed in response to complex plate tectonic adjustments following subduction initiation in the Eocene. Although there has been extensive exploration for offshore petroleum resources, and more than 54 research cruises have investigated or transited the region since 1993, a comprehensive regional geological map, including the deep marine areas, has not been available at a scale that permits quantitative analysis of the basin history. We present the first map that depicts interpreted assemblage- and formation-level lithostratigraphic units correlated across the marine basins and adjacent land masses. The mapped assemblages and large-scale formations are based on a compilation of land-based geological maps, marine geophysical data (hydroacoustics, magnetics, and gravity) integrated with the results of geological sampling, ocean drilling, seismic surveys, and seabed observations.</p><p>More than 400,000 km<sup>2 </sup>of the map area covered by ship-based multibeam and other geophysical data were inspected to derive the offshore geological units. In areas with limited data, the units were extrapolated from well-documented formations in adjacent regions with more complete information, including on land. This approach follows closely the techniques used for remote predictive mapping in other regions of the Earth where geological information is sparse. Geological boundaries were constrained by ship-based multibeam data reprocessed at 35-m to 50-m resolution and integrated with the Global Multi-Resolution Topography (GMRT) gridded at 100 m. Lithotectonic assemblages were assigned on the basis of plate structure, crustal type and thickness, age, composition, and sedimentary cover and further refined by bathymetric and geophysical data from the literature and cruise reports. The final compilation is generalized and presented here at 1:1 М. Our new approach integrates conventional mapping on land with remote predictive mapping of the ocean floor.</p><p>The newly compiled geological map illustrates the diversity of assemblages in the region and its complex geodynamic evolution. The resolution of our map allows to perform quantitative analyses of area-age relationships and thus crustal growth. Further geoscientific analyses may allow to estimate the regional mineral potential and to delineate permissive areas as future exploration targets.</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.027
GPT teacher head0.198
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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