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Record W2964139481 · doi:10.1111/1556-4029.14136

Resolving Reflection and Resolution in 3D Imaging of Fresh Bone

2019· article· en· W2964139481 on OpenAlexaff
Rabbia Imran, Tracy L. Rogers

Bibliographic record

VenueJournal of Forensic Sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReflection (computer programming)Diffuser (optics)Light reflectionResolution (logic)ScannerOpticsMaterials scienceHigh resolutionComputer visionBiomedical engineeringComputer scienceArtificial intelligenceLight sourceOptoelectronicsGeologyMedicineRemote sensingPhysics

Abstract

fetched live from OpenAlex

Structured light scanning is a noninvasive, accurate, and cost-effective 3D imaging technique, but due to reflection issues is yet to be utilized for tool mark analysis on fresh bone. During imaging, reflection from shiny surfaces, such as greasy bone, disrupts image formation. This study tested the David SLS-1 scanner's ability to image saw marks and explored six strategies to reduce reflection by [1] dulling the surface or [2] altering the projected light. The surface was dulled by freezing, talcum powder, dulling spray, or compressed air. The projected light was altered with a diffuser or limited to single pattern-coded. Results demonstrated that the resolution was insufficient for capturing minute details of striae. All six tests failed to reduce reflection sufficiently to produce complete images, but projecting vertical pattern-coded light showed the most promise. Future research is required concentrating on enhancing resolution and exploring the role of pattern-coded light in reducing reflection.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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 teacher head, 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

Citations4
Published2019
Admission routes1
Has abstractyes

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