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Record W4244118667 · doi:10.22215/etd/2013-10794

3D Laser Imaging and Modeling of Iron Meteorites and Tektites

2013· dissertation· en· W4244118667 on OpenAlexaff
Christopher Fry

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsCarleton University
Fundersnot available
KeywordsMeteoriteLaserGeologyWeatheringMineralogyMaterials scienceAstrobiologyOpticsGeochemistryPhysics

Abstract

fetched live from OpenAlex

3D laser imaging is a non-destructive method devised to calculate bulk density by creating volumetrically accurate computer models of hand samples.The focus of this research was to streamline the imaging process and to mitigate any potential errors.3D laser imaging captured with great detail (30 voxel/mm 2 ) surficial features of the samples, such as regmaglypts, pits and cut faces.Densities from 41 iron meteorites and 9 splash-form Australasian tektites are reported here.The laser-derived densities of iron meteorites range from 6.98 to 7.93 g/cm 3 .Several suites of meteorites were studied and are somewhat heterogeneous based on an average 2.7% variation in inter-fragment density.Density decreases with terrestrial age due to weathering.The tektites have an average laser-derived density of 2.41+0.11g/cm 3 .For comparison purposes, the Archimedean bead method was also used to determine density.This method was more effective for tektites than for iron meteorites.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.215
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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