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Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9780470316436openalex-snapshot
Linear Statistical Inference and its Applications
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Estimating the Probability of Occurrence of Mineral Deposits from Multiple Map Patterns
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Application of Recent Developments of Regression Analysis in Regional Mineral Resource Evaluation
inferredcites →https://doi.org/10.1007/978-94-009-4029-1_2openalex-snapshot
Poisson Regression Analysis and its Application
inferredcites →https://doi.org/10.1007/bf01028881openalex-snapshot
Regression models for estimating mineral resources from geological map data
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Combining indicator patterns in weights of evidence modeling for resource evaluation
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Automatic contouring of geological maps to detect target areas for mineral exploration
inferredcites →https://doi.org/10.1016/0743-9547(90)90028-copenalex-snapshot
Arc magmatism and mineralization in North Luzon and its relationship to subduction at the East Luzon and North Manila Trenches: reply
inferredcites →https://doi.org/10.1023/a:1010147818806openalex-snapshot
Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines
inferredcites →https://doi.org/10.1023/a:1014416319335openalex-snapshot
Spatial Association of Mineral Occurrences and Curvilinear Geological Features
inferredcites →https://doi.org/10.1080/00401706.1996.10484485openalex-snapshot
Applied Logistic Regression Analysis
inferredcites →https://doi.org/10.1093/oso/9780195085938.003.0007openalex-snapshot
Weights Of Evidence Modeling And Weighted Logistic Regression For Mineral Potential Mapping
inferredcites →https://doi.org/10.2113/gsecongeo.74.6.1420openalex-snapshot
Fluid inclusion and geochemical studies of vein gold deposits, Baguio District, Philippines
inferredcites →https://doi.org/10.2113/gsecongeo.74.8.1852openalex-snapshot
Gold deposition in the Baguio gold district and its relationship to regional geology
inferredcites →https://doi.org/10.2113/gsecongeo.91.2.243openalex-snapshot
Epithermal gold mineralization, Acupan, Baguio District, Philippines; geology, mineralization, alteration, and the thermochemical environment of ore deposition
inferredcites →https://doi.org/10.2307/2529568openalex-snapshot
Linear Statistical Inference and Its Applications.
inferredcites →https://doi.org/10.4095/103392openalex-snapshot
Computer program for the logistic model to estimate the probability of occurrence of discrete events
inferredcites →https://doi.org/10.4095/128059openalex-snapshot
Weights of evidence modelling: a new approach to mapping mineral potential
inferredcites →https://doi.org/10.5860/choice.29-5134openalex-snapshot
Epithermal gold in the Philippines: island arc metallogenesis, geothermal systems and geology
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3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China
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A Review of Integration Techniques of Multi-Geoscience Data-Sets in Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1190/image2022-3750705.1openalex-snapshot
A step towards the automatization of predictive geological mapping
inferred← citeshttps://doi.org/10.22059/geope.2019.284414.648488openalex-snapshot
A clustering approach for mineral potential mapping: A deposit-scale porphyry copper exploration targeting
inferred← citeshttps://doi.org/10.2478/s13533-012-0183-yopenalex-snapshot
A case study for the integration of predictive mineral potential maps
inferred← citeshttps://doi.org/10.3390/min10020102openalex-snapshot
Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China
inferred← citeshttps://doi.org/10.3390/min12050616openalex-snapshot
Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data
inferred← citeshttps://doi.org/10.3390/min12060669openalex-snapshot
Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province
inferred← citeshttps://doi.org/10.3390/min12091174openalex-snapshot
Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China
inferred← citeshttps://doi.org/10.3390/min13101267openalex-snapshot
Mineral Prospectivity Mapping for Orogenic Gold Mineralization in the Rainy River Area, Wabigoon Subprovince
inferred← citeshttps://doi.org/10.3390/min13101302openalex-snapshot
Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China
inferred← citeshttps://doi.org/10.3390/min15040411openalex-snapshot
Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms
inferred← citeshttps://doi.org/10.3390/min16010099openalex-snapshot
Genesis of the Hadamengou Gold Deposit, Northern North China Craton: Constraints from Ore Geology, Fluid Inclusion, and Isotope Geochemistry
inferred← citeshttps://doi.org/10.3390/rs15020439openalex-snapshot
Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
inferred← citeshttps://doi.org/10.3390/rs17152689openalex-snapshot
Current Progress in and Future Visions of Key Technologies of UAV-Borne Multi-Modal Geophysical Exploration for Mineral Exploration: A Scoping Review
inferred← citeshttps://doi.org/10.36074/logos-31.03.2023.70openalex-snapshot
APPLIED STATISTICS AND MACHINE LEARNING IN EARTH SCIENCES: CHOOSING THE RIGHT APPROACH FOR MODERN SCIENTIFIC RESEARCH
inferred← citeshttps://doi.org/10.4236/jgis.2012.42011openalex-snapshot
Mineral Prospectivity Mapping Method Integrating Multi-Sources Geology Spatial Data Sets and Case-Based Reasoning
inferred← citeshttps://doi.org/10.5194/npg-20-445-2013openalex-snapshot
Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping
inferred← citeshttps://doi.org/10.70675/e63e5262z4cf5z4670z997azf3ce04778adaopenalex-snapshot
Highlighting mineralized geological environments through a new Data-driven predictive mapping approach
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