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Record W7079630581 · doi:10.26108/knz7-1b51

Re-interpretation of the geology of the Cape Breton Highlands using combined remote sensing and geological databases

2001· article· en· W7079630581 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2001
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsGeologic mapRadiometric datingTerraneDigital elevation modelNova scotiaElevation (ballistics)Cape

Abstract

fetched live from OpenAlex

Relationships among the varied rock units of the Cape Breton Highlands as revealed by geological mapping are not everywhere clear, in part due to limited exposure and also to difficulty of access in some areas. This study was undertaken to try to improve on the geological interpretation of the highlands, especially in those problematic areas, by combining geological and remote sensing databases. The geological database was assembled based mainly on published maps. Remotely sensed information used in the study included Radarsat S7, Landsat TM, gravity, magnetic (vertical gradient and total field) and radiometric data. A Digital Elevation Model (DEM) was constructed from 174 Nova Scotia 1:10,000-scale contour map sheets. The geological, geophysical, and remotely sensed data were integrated into a Geographical Information System, and the resulting database was used to evaluate the various geological interpretations of the highlands. The combined DEM and vertical gradient image and a classified image based on radiometric data proved most useful in the interpretation process. The key result of the study was to demonstrate how the combined data sets can lead to improved geological interpretation, in particular with respect to geological contacts between known map units in poorly exposed areas, and in the location of major faults and postulated terrane boundaries. Granitoid map units were best detected and distinguished with the radiometric data.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.273

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 science0.0010.001
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 head0.027
GPT teacher head0.253
Teacher spread0.227 · 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 designSimulation or modeling
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
Published2001
Admission routes1
Has abstractyes

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