Multi-method 2D and 3D reconstruction of gold grain morphology in alluvial deposits: a review and application to the Rivière du Moulin (Québec, Canada)
Bibliographic record
Abstract
Abstract The aim of this paper is to document and compare the 2D qualitative and semi-quantitative methods currently used to describe the shape of gold grains in fluvial environments with the 3D quantitative methods using X-ray microtomography and SEM photogrammetry. These 3D methods are used to compute flatness, roundness, convexity, sphericity and ellipticity shape descriptors of 13 gold grains from the Rivière du Moulin (Québec, Canada) in order to quantify the morphological change along 9 km of fluvial transport. Gold grains have moderate to high values of flatness, compactness, sphericity and ellipticity indices that do not change significantly with distance of transport, whereas the roundness increases during transport. Gold grains are used to compare 2D and 3D methods, and the results show small differences (<8%) when shape descriptors are computed using image analysis software, whereas the difference (up to 70%) is more important for 2D measurements performed by a human operator. For application and characterization on a large set of gold grains, the 2D methods offer the advantage of speed, whereas, for a more detailed study on a limited number of gold grains, 3D methods enable estimation of the volume and yield more detailed shape descriptor changes during fluvial transport.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".