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Record W3035180551 · doi:10.3997/2214-4609.201902462

Metallogenic Mechanism of the North Qinhang Belt, South China, from Gravity and Magnetic Inversions

2019· article· en· W3035180551 on OpenAlexaff
Y. Liu, Qingtian Lü, Colin G. Farquharson, Jiayong Yan

Bibliographic record

Venue25th European Meeting of Environmental and Engineering Geophysics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGeologyLithosphereSubductionTectonicsDiapirGeochemistryMantle (geology)ProterozoicGravity anomalyLineamentUpwellingVolcanoUltramafic rockGeophysicsPetrologySeismologyPaleontology

Abstract

fetched live from OpenAlex

Summary Qin—Hang metallogenic belt are famous tectonic units and important rich mineral resources in south China. Due to few geophysical data, there was previously lack of comprehensive geophysical research in this area. In this paper, based on priori geological information and exploration data, regional structures are divided and volcanic rocks are delineated, even the deep dynamic process and mineralization mechanism have been speculated from gravity and magnetic data inversion. The boundary between Cathaysia plate and the Yangtze plate, and five geological blocks, even major fractures can be identified, which is the main channel of magma upwelling. Some deep and large faults control magmatic activity, and those secondary faults control regional mineralization. Negative gravity anomaly can be used as a basis for searching magmatic rocks. Here abundant basic - ultrabasic magma in 220~250 Ma period, is the result of lithosphere stretching, thinning and mantle upwelling. It is a vital metallogenic mechanism in the belt that ancient subduction zone after modification or superimposed mineralization in yanshan epoch. Further, the “proterozoic + Mesozoic” bimodal age phenomenon of porphyry and its associated ore deposits in this region can be confirmed.

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 score0.035
Threshold uncertainty score0.341

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 head0.004
GPT teacher head0.120
Teacher spread0.116 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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