Automated Gold Grain Counting. Part 2: What a Gold Grain Size and Shape Can Tell!
Bibliographic record
Abstract
Glacial drift exploration methods are well established and widely used by mineral industry exploring for blind deposit in northern territories, and rely on the dispersion of mineral or chemical signal in sediments derived from an eroded mineralized source. Gold grains themselves are the prime indicator minerals to be used for the detection of blind gold deposits. Surprisingly, very little attention has been dedicated to the information that size and shape of gold grain can provide, other than a simple shape classification based on modification affecting the grains that are induced in the course of sediment transport. With the advent of automated scanning electron microscope (SEM)-based gold grain detection, high magnification backscattered electron images of each grain are routinely acquired, which can be used for accurate size measurement and shape analysis. A library with 88,613 gold grain images has been accumulated from various glacial sediment surveys on the Canadian Shield and used to detect trends in grains size and shape. A series of conclusions are drawn: (1) grain size distribution is consistent among various surveys and areas, (2) there is no measurable fine-grained gold loss due to natural elutriation in ablation or reworked till, or during the course of reverse circulation drilling, (3) there is no grain size sorting during glacial transport, severing small grains from large ones, (4) shape modification induced by transport is highly dependent on grain size and original shapes, and (5) the use of grain shape inherited from neighboring minerals in the source rocks is a useful feature when assessing deposit types and developing exploration strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".