MétaCan
Menu
Back to cohort
Record W4229024483 · doi:10.1144/geochem2021-088

Analysis of geochemical patterns in a soil profile over mineralized bedrock

2022· article· en· W4229024483 on OpenAlexaff
Jie Yang, Zhaoxian Yuan, Eric Grunsky, Qiuming Cheng, Shubin Zhou

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2022
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Waterloo
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsBedrockMineralization (soil science)GeologyPrincipal component analysisRegolithMineralogySoil waterGeochemistryWeatheringSoil scienceGeomorphology

Abstract

fetched live from OpenAlex

Transported soils cause difficulties in the identification of geochemical anomalies. It has been demonstrated that the joint application of local singularity analysis (LSA) and principal component analysis (PCA) can identify geochemical anomalies effectively, especially in regolith-covered areas. However, more convincing evidence is needed to explain the reasons for this. In this study, a soil profile overlying several mineralized veins cutting through bedrock was analysed in situ using a portable X-ray fluorescence spectrometer. The patterns of two mineralization-related elements, Cu and Mo, were analysed. The results revealed that the element concentrations of the soil sharply decreased as the distance from the bedrock increased, and this relationship can be described by a power-law model. LSA enhanced several vein-like anomalies corresponding to the mineralization veins in the bedrock, and the presence of vertically elongated weak anomalies in the soil indicates the migration of ore elements originating from the underlying bedrock through the soil. The statistics show that the patterns of the local singularity index (LSI) are stable at different depths and in different media, whereas the concentration patterns are not. In addition, the mineralization-related elements have a higher correlation coefficient for the LSI than for the concentration. Since a previous simulation study determined that a mineralization-indicative first principal component prefers that the variables have a close relationship and that the variables have similar patterns in different geological objects, the patterns discovered in this study explain why LSA is effective in identifying geochemical anomalies, especially when combined with PCA. Supplementary material: the high-resolution photo, the element concentration data and the lithologic data of the profile are available at https://doi.org/10.6084/m9.figshare.c.5957122 Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGeochemistry Exploration Environment AnalysisSame topicGeochemistry and Geologic MappingFrench-language works237,207