Iron meteorite bulk densities determined via 3‐D laser imaging
Bibliographic record
Abstract
Abstract This study tested the feasibility of using 3‐D laser imaging to measure the bulk density of iron meteorites. 3‐D laser imaging is a technique in which a 3‐D model of an object is built after aligning and merging individual detailed images of its surface. Assuming that the mass of the object is known, the volume of the model is calculated by software and an estimate of bulk density can be obtained by dividing mass by volume. The 3‐D laser imaging technique was used to determine the density of 46 fragments from 11 different iron meteorites. The technique proved to be robust and was applied successfully to study samples ranging close to four orders of magnitude in mass (8 g to 156 kg) and exhibiting a variety of surface textures (e.g., cracks, regmaglypts), reflectivities (e.g., polished surfaces, fusion crust, rust), and morphologies (e.g., sharp angular edges, shrapnel tendrils). Three metrics were considered to estimate the error associated with density measurements: the range accuracy of the laser camera, image alignment error, and inter‐operator variability during model building. Inter‐operator variability was the largest source of error and was highest when assembling models of samples which either lacked distinctive features or were very complex in shape. Excluding two anomalous Zagora samples where silicate inclusions might have lowered density, the densities measured using 3‐D laser imaging ranged from 6.98 to 7.93 g cm−3, consistent with previous studies. There is overlap between bulk density and iron meteorite class, and therefore bulk density cannot be used in isolation as a classification criterion. It is a good indicator, however, of weathering effects and of the potential presence of low‐density inclusions.
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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 | 0.000 |
| 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".