Exploration geophysics at the Pyhäsalmi mine and grade control work of the Outokumpu Group
Bibliographic record
Abstract
Abstract The power of geophysics is often realized while surveying barren exploration holes. Integrated interpretation of borehole electromagnetic (EM) and lithogeochemical data led to the discovery of a new volcanogenic massive sulfide (VMS) ore deposit at 500 m depth in the Pyhäsalmi area, which belongs to the Main Sulfide ore belt in Finland. In the deep exploration program, wide-band multifrequency EM ground surveys were successfully used to detect both new ore lenses and geological structures. Mise-a-la-masse (MAM) borehole and ground surveys as well as borehole EM surveys were effectively used to correlate intersections between drill holes and to locate new orebodies. The latest modeling of MAM data resulted in an exploration target at 700 m depth. The use of geophysics for exploration has been extended to mine production at Outokumpu. Geophysical logging detects ore-waste boundaries, reduces expensive core drilling, and obtains physical property information quickly on ore intersections. Depending on ore type, geophysical borehole logging can also be applied to classify mineralization, interpret lithology, and sometimes to transform physical responses to metal grades in ore. At the Pyhäsalmi zinc-copper-sulfur mine, density logging in percussion boreholes is used to locate mineable ore boundaries and to classify drillhole intersections as massive or semimassive sulfide ore types. Pyrrhotite-bearing zones are separated from other sulfides by inductive conductivity logs. The use of geophysical logging for grade estimation and control has been most effective in the nickel mines at Enonkoski, Finland, and Namew Lake, Canada (using conductivity logs), and in the Kemi chromium mine, Finland (using gamma-gamma density logs).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".