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Record W3135450182 · doi:10.3390/rs13050903

Application of Airborne Electromagnetics and Magnetics for Mineral Exploration in the Baishiquan–Hongliujing Area, Northwest China

2021· article· en· W3135450182 on OpenAlexaboutno aff
Shengjun Liang, Siyuan Sun, Hongfei Lu

Bibliographic record

VenueRemote Sensing · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
FundersChina Geological SurveyNational Natural Science Foundation of China
KeywordsProspectingGeologyMineral explorationElectromagneticsExploration geophysicsMining engineeringRemote sensingGeophysicsEngineeringEngineering physics

Abstract

fetched live from OpenAlex

Airborne electromagnetics is an effective and efficient exploration tool in shallow mineral exploration for its high efficiency and low cost. In 2016, airborne electromagnetic and airborne magnetic surveys have been carried out at the border of Xinjiang Uygur Autonomous Region and Gansu Province, the Northwest China. With an integrated system, the airborne electromagnetics and airborne magnetic data were collected simultaneously by AreoTEM-IV system from Aeroquest International Limited in Vancouver, BC, Canada, and the CS3 Cesium Vapor magnetometer from Scintrex in Concord, ON, Canada. About 3149 line-km of both data with 250 m line space were acquired. After data processing, the comprehensive analysis and interpretation of resistivity and magnetic anomalies has been carried out to infer lithological structure and outline the potential ore deposits. Verified by the ground surveys, seven outlined anomalies are consistent with the known ore sites, and one new gold deposit and several mineralization clues were found. The prospective reserves of gold are expected to exceed 10 tons. Besides, some prospecting target areas were outlined as the possible locations of copper–nickel deposits. The successful case shows the airborne magnetic data accords with geological structures, and the airborne electromagnetic method is effective in finding metal mineral resources, which can help to quickly identify potential ore targets with no surface outcrop.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.248

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)0.0000.000

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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 designOther design
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

Citations22
Published2021
Admission routes1
Has abstractyes

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