What lies beneath? A reflection on the porphyry copper exploration model
Bibliographic record
Abstract
SummaryElectrical methods have been applied to the search for porphyry copper and IOCG deposits since the early 1950s. While there is a generally accepted model of disseminated sulfides giving rise to a chargeability response, no clear association has been attached to what EM surveys may be responding to. Work in the early 1990s (Nickson 1993) showed the well-developed supergene blankets over a porphyry copper could be conductive; this observation was however, never applied formally to generally accepted porphyry targeting models. The presence of other conductive zones associated with porphyry copper deposits is even less well studied. On the geological side, while there is a vast body of literature describing porphyry copper deposits and how to discover them, in very few cases do these studies even speculate if anomalous concentrations of sulfides could be conductive. On the geophysical side, observations of unexpected conductivity associated with porphyry systems is sometimes noted but these observations typically stop short of suggesting that there could be a more general observation made that a new class of geophysical feature should be defined. The present study is felt to have gathered a sufficient number of case studies which show that a significant number of porphyry copper deposits posse a mineralogical character which can be identified with EM techniques. This thesis can have significant implications as to how porphyry copper are explored for, especially those at depths >500 m, a generally accepted cut-off for IP techniques.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".