Overcoming survival bias in targeting mineral deposits of the future: Towards null and negative tests of the exploration search space, accounting for lack of visibility
Bibliographic record
Abstract
• Survival bias concept is adapted for mineral exploration. • Next generations of mineral deposits are defined. • Analysis of the exploration search spaces in terms of Null and Negative tests is proposed. • Targeting mineral deposits of the future is discussed. • The findings have implications for the future development of exploration information systems. Broad consensus exists amongst mineral explorers that most outcropping mineral deposits have been found. The next generation of discoveries will rely on our ability to recognize the subtle or cryptic signals of deep-seated deposits. Exploration targeting under such conditions requires greater knowledge of the processes that formed the targeted mineral deposit types and new or improved exploration methods designed to effectively test for buried mineralization. Survival bias is a form of selection bias that is defined as the logical error resulting from neglecting data or information because of their “lack of visibility”. In this study, “lack of visibility” refers to situations where (i) mineral explorers ignore or overlook particular terrain because it lacks or contains only weak signals of a mineralizing system, and (ii) such areas are excluded from further exploration as the existing data or information neither confirm nor support the targeting model. Therefore, it is critical to more comprehensively analyze a search space to more confidentially determine whether a terrain without the desired targeting signals satisfies the criteria of a null or negative test. The idea for this study is based on the notion that if a buried mineral deposit was present in an overlooked terrain it would nevertheless comprise distinctive geological features and targeting signals to guide the explorer, although, more likely than not, these signals would be very weak. Here we used a porphyry copper (Cu) district in Iran to explain and illustrate the adaptation of the survival bias concept. More specifically, in this study we tested the usefulness of a recently proposed targeting criterion, namely sites of potential focused fluid flux, as an input to mineral prospectivity analysis and exploration targeting. The findings of our study have implications for the future development of regional- to global-scale exploration information systems (EIS), designed to improve the performance of mineral exploration targeting.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".