Application of concentration gradient coefficients in mining geochemistry: A comparison of copper mineralization in Iran and Canada
Bibliographic record
Abstract
In this work, the concentration gradient (CG) analysis of local-scale exploration for Porphyry-Cu deposits is applied in two zones using the G(Vz) index (CG(Zn*Pb)/CG(Cu*Mo)). The first zone is covered by a 1:2000 map of the Sungun and Astamal areas in NW Iran and the second one in the Inza area in British Columbia, Canada. The rock samples are taken from Sungun and Astamal and the soil samples are taken from Inza. The Inza samples are analyzed for Cu, Pb, Zn, and Mo elements by the atomic absorption method, while the rock samples of Astamal and Sungun are analyzed for Cu, Pb, Zn, Mo, Ag, As, and Sb elements. The indices of gradient geochemical zonality (G(Vz)) of multi-elements around the mineral deposits and their spatial associations with particular geological, geochemical, and structural factors are the critical aspects that must be considered in mineral exploration. The values for the G(Vz) indices allow a distinction between the sub-ore and supra-ore anomalies, which are associated with Zone Dispersed Mineralization (ZDM) and Blind Mineralization (BM), respectively. For a comparative identification of BM and ZDM, a supra-ore (Pb*Zn) anomaly, a sub-ore (Cu*Mo) anomaly, and Vz maps are used in place of the mining geochemistry representing the supra-ore gradient anomaly, sub-ore gradient anomaly and G(Vz) map. The G(Vz) model outperforms the Vz model. The introduced technique allows for a computational distinction between the BM and ZDM ore mineralizations without exploration drilling. Prior to writing this paper, the blind porphyry-Cu mineralization was intersected at depth through borehole exploration in a highly prospective zone delineated by the G(Vz) model. The results obtained confirm the usefulness of the G(Vz) modeling for local-scale targeting of blind mineral deposits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".