3‐D laser images of splash‐form tektites and their use in aerodynamic numerical simulations of tektite formation
Bibliographic record
Abstract
Abstract Ten splash‐form tektites from the Australasian strewn field, with masses ranging from 21.20 to 175.00 g and exhibiting a variety of shapes (teardrop, ellipsoid, dumbbell, disk), have been imaged using a high‐resolution laser digitizer. Despite challenges due to the samples’ rounded shapes and pitted surfaces, the images were combined to create 3‐D tektite models, which captured surface features with a high fidelity (≈30 voxel mm−2) and from which volume could be measured noninvasively. The laser‐derived density for the tektites averaged 2.41 ± 0.11 g cm−3. Corresponding densities obtained via the Archimedean bead method averaged 2.36 ± 0.05 g cm−3. In addition to their curational value, the 3‐D models can be used to calculate the tektites’ moments of inertia and rotation periods while in flight, as a probe of their formation environment. Typical tektite rotation periods are estimated to be on the order of 1 s. Numerical simulations of air flow around the models at Reynolds numbers ranging from 1 to 106 suggest that the relative velocity of the tektites with respect to the air must have been <10 m s−1 during viscous deformation. This low relative velocity is consistent with tektite material being carried along by expanding gases in the early time following the impact.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".