Measuring the bulk density of meteorites nondestructively using three‐dimensional laser imaging
Bibliographic record
Abstract
The fragile and unique nature of meteoritic material requires a method of density measurement that is accurate yet nondestructive. This is difficult to achieve using conventional methods. In this study, the bulk density of eleven meteorite fragments which vary in shape, size, surface roughness, porosity, and reflectance has been determined using three‐dimensional (3‐D) laser imaging. An auto‐synchronized laser camera raster scanned the surface features of each meteorite without contact and to a high degree of precision. Visualization software was used to align several scans into a closed model and to compute its volume. The mass having been predetermined, the density was then easily computed. Three‐dimensional laser imaging is the least invasive method of density measurement currently in use. The precision of the approach is less than 1%. The densities determined using 3‐D laser imaging compare very well with previously published values, based on a variety of different measurement techniques. The average difference is 3.4% and can be attributed to the presence of heterogeneities and to the limited amount of comparative data available. For nine out of the eleven samples studied, the densities determined using 3‐D laser imaging are higher than previously published results. Friction between fluid and container, and bead compaction might have led to an overestimation of the volume measured using Archimedean methods. In addition to volume measurements, 3‐D images of rock samples can yield detailed information on surface properties from a distance. The technique could be used for semiautonomous planetary geological exploration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".