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Record W2465929826

The use of Topographic-bathymetric Lidar to Enhance Geological Structural Mapping in Maritime Canada

2016· article· en· W2465929826 on OpenAlexaffvenueabout
Tim Webster, Kevin G. McGuigan, Nathan Crowell, Kate Collins, Candace MacDonald

Bibliographic record

VenueGeoscience Canada · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsNova Scotia Community College
Fundersnot available
KeywordsGeologyBedrockOutcropBathymetryReefLidarShoreSubmarine pipelineGeomorphologyGlacial periodLandformOceanographyRemote sensing
DOInot available

Abstract

fetched live from OpenAlex

An airborne topo-bathymetric lidar survey was conducted in Cape John, on the north shore of Nova Scotia, Canada in September 2014 using the shallow water Leica AHAB Chiroptera II sensor and revealed new bedrock features that were not discovered using previous mapping methods. A thick blanket of glacial till covers the bedrock on land and outcrops are exposed only along the coastal cliffs and offshore reefs. The seamless land-seabed digital elevation model (DEM) produced from the lidar survey revealed significant bedrock outcrop offshore where ocean currents have removed the glacial till, a significant finding that was hitherto hidden under the sea surface. Several reefs were identified offshore as well as a major fold structure where block faulting occurs along the limbs of the fold. The extension of the Malagash Mine Fault located ~10 km west of Cape John is proposed to explain the local folding and faulting visible in the submerged outcrops. The extension of this fault is partially visible on land where it is obscured by glacial till cover and its presence is supported by the orientation of submerged bedding and lineaments on both the south and north sides of Cape John. This paper demonstrates how near-shore high resolution topography from bathymetric lidar can be used to enhance and refine geological mapping.

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 score0.429
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.207
Teacher spread0.195 · 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".

Quick stats

Citations0
Published2016
Admission routes3
Has abstractyes

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