Missed hits or near misses: determining how many samples are necessary to confidently detect nugget-borne mineralization
Bibliographic record
Abstract
The probability of collecting a sample containing at least one large nugget from an exploration prospect (and thus detecting the nugget-borne mineralization) can be calculated using Poisson statistics and an equant grain model that describes the sampling characteristics of mineralization containing a range of nugget sizes. This procedure requires an estimate of the mass-weighted, average (effective) nugget grain size in the mineralized material, and an estimate of the (expected) grade of mineralization. Using these parameters, the number of effective nuggets in an equivalent equant grain model that describes the sampling characteristics of mineralization can be determined and used to estimate the Poisson probability of collecting at least one large nugget in a real sample. With this information, the probability of collecting m large nugget-bearing samples from a set of n samples can be determined using binomial statistics, providing the explorationist with an estimate of how well a prospect containing nugget-borne mineralization will be assessed using those n samples. Software can be used to perform the associated calculations.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".