An Empirical Geophysical Model for Porphyry Copper Deposits in the Laramide Copper Province
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".