Bibliographic record
Abstract
Abstract This paper reviews and analyzes the role played by changes in mineral exploration practice on the discovery record worldwide, over a timeframe of roughly the last 50 years. Geologic field methods have remained relatively unchanged, although general geologic theory as well as some empirical and genetic ore deposit models and related concepts have undergone major revisions that have had significant but unquantifiable effects on the exploration process. The principal geochemical and geophysical methods employed in the 1950s and 1960s remain preeminent, notwithstanding the burgeoning sophistication of analytical techniques and geophysical instrumentation, and the exponential increases in data-processing capacity. Remote sensing and data management and modeling technology have also both advanced apace over the last two decades. The evolution of these earth-science disciplines affected the practicalities of mineral exploration in various ways, as demonstrated using porphyry Cu, volcanogenic massive sulfide (VMS), sediment-hosted (Carlin-type) Au, epithermal Au, orogenic (mesothermal) lode Au, and magmatic Ni-Cu deposits as examples, although constancy in search procedures prevailed over radical change. Advances in the geologic, geochemical, geophysical, and remote-sensing fields do not seem to have greatly influenced the discovery record, at least where the most reliable compilations are available, for the circum-Pacific region over the last 35 years. Discovery has resulted mainly from routine fieldwork complemented by conventional geochemistry, and to a far lesser degree, from ground geophysics. Nevertheless, geophysics has made a greater contribution to discovery of the increasing number of deposits concealed beneath pre- and postmineralization cover. In Precambrian shield areas, such as Canada, Australia, and Scandinavia, with a dominance of different ore deposit types and distinct physiographic conditions, airborne geophysics has clearly played a more influential role in discovery, particularly of VMS, magmatic Ni-Cu, unconformity related U, and diamond deposits as well as greatly contributing to regional geologic understanding. Perpetuation of broadly the same exploration approach, using tried-and-tested field-based methods, is strongly advocated, especially in the case of green-field programs. Continued innovative exploration of the world’s premier metallogenic belts and provinces must be combined with search for new, highly endowed frontier regions. While geologic and geochemical modeling remains vital for brown-field exploration, a greater future role for geophysics and grid and fence drilling is envisioned. Technologic advances in all these fields will undoubtedly facilitate an increasing number of exploration tasks, particularly data gathering, handling, and analysis, but are thought unlikely to dramatically change the discovery process and could even have a negative influence if not used to enhance productive field time. Future exploration success, especially under deep cover, demands more predictive ore deposit models and geochemical and geophysical methods that are better able to penetrate the overburden. The overall quality and inventiveness of exploration programs must improve, however, if the perceived decline in discovery rate and rise in discovery cost are to be remedied. Carefully targeted research on all fronts is necessary and should be welcomed.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".