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Record W6969107884 · doi:10.5281/zenodo.7980118

An Empirical Geophysical Model for Porphyry Copper Deposits in the Laramide Copper Province

2023· article· en· W6969107884 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsTeck (Canada)
Fundersnot available
KeywordsPorphyry copper depositMineralization (soil science)MagmatismCopperTrace element

Abstract

fetched live from OpenAlex

The Laramide copper province is located in southwestern North America, covering parts of Arizona, New Mexico, and Texas in the U.S, in addition to Sonora, Chihuahua, Sinaloa and Baja California in Mexico. Porphyry copper mineralization is associated with Laramide age (~80-45 Ma) magmatism and has been estimated to represent ~300 million tonnes of copper metal, making it a globally significant accumulation of the red metal. The geological and geochemical manifestation of these mineralizing systems have been well documented. Specifically, exploration models based on alteration zonation and trace element geochemistry have been developed and successfully deployed in the province since the late 1960's. As a result of post Laramide extension and deposition, much of the province is covered by post-mineral rocks or sediments, and it can be argued that the greatest residual potential for future discoveries is located within the covered regions. Consequently, geophysical datasets are playing a more prominent role in integrated targeting of porphyry systems. In this extended abstract, we present a series of observations and interpretations of geophysical data from various deposits in the province with the goal of developing an empirical model to guide selection of geophysical method, interpret subsequent results and ultimately contribute to future exploration success.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.028
GPT teacher head0.252
Teacher spread0.223 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicRailway Engineering and DynamicsFrench-language works237,207