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Geology mapping with an emphasis on the Quaternary in the Swiss Prealps and Molasse Plateau

2020· article· en· W3043002694 on OpenAlexaboutno aff
Valérie Baumann, Marc‐Henri Derron, Jean‐Luc Epard, Michel Jaboyedoff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyDigital elevation modelQuaternaryMolassePlateau (mathematics)BoreholeElevation (ballistics)BedrockGeomorphologyLidarGeochemistryPaleontologyRemote sensingGeometry

Abstract

fetched live from OpenAlex

The main goal of this project is to harmonise the different geological maps (scale 1:25.000) and to improve the Quaternary mapping of the region of “canton de Vaud” in Switzerland using a high resolution LiDAR digital elevation model, and geophysical or boreholes data. We present here the results for the geologic mapping of two test areas: one in the Prealps and the second in the Molasse Plateau. Detailed geological maps (scale 1:25.000) have been produced during the XX century for the whole region. During the last Late Glacial Maximum (LGM) the canton de Vaud area was covered by ice sheets, then soils and loose rock deposits were formed toward the end of ice age, however the Quaternary formations are sometimes not represented especially when their thickness is only of a few meters and the interpretation of geomorphologic features with aerial photographs was difficult in areas covered by forest. In recent years, the high-resolution digital elevation model derived from high resolution LiDAR data with the possibility to remove the trees in the forested areas offers the possibility to detect and interpret new morphologies. In this study, different LIDAR-derived hillshade maps have been used to improve the delimitation of bedrock and Quaternary formation through morphological feature analyse. Borehole data gave us fundamental data about geology and stratigraphy and field surveys were performed for selected places. Additionally, a terrain classification system first developed in Canada (Cruden and Thomson, 1987) was used to add information for each polygon like genetic material, surface expression, modifying processes and stratigraphic data. All the mapping was performed in a GIS (Geographic system information) environment. Detailed bedrock and Quaternary mapping will provide very good information for the management of the resources, land planning and geo-hazards. The additional information (terrain classification) for each polygon allow us to create different thematic maps starting from the geological map. Reference: Cruden, D. M., and S. Thomson. Exercises in terrain analysis. Pica Pica Press, 1987.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.246
Teacher spread0.196 · 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 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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