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Record W2161695818 · doi:10.1093/ps/85.4.706

Use of Axial X-Ray Microcomputed Tomography to Assess Three-Dimensional Trabecular Microarchitecture and Bone Mineral Density in Single Comb White Leghorn Hens

2006· article· en· W2161695818 on OpenAlexaff
M.A. Martínez-Cummer, Richard J. Heck, S. Leeson

Bibliographic record

VenuePoultry Science · 2006
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBone mineralTomographyCancellous boneMaterials scienceX-rayTrabecular boneBone densityAnatomyX-ray microtomographyBiomedical engineeringOpticsOsteoporosisPhysicsMedicinePathology

Abstract

fetched live from OpenAlex

Axial x-ray microcomputed tomography is a cost-effective technique with the potential to assess bone mineral density (mg/cc) in both cortical and cancellous bone in Single Comb White Leghorn hens. The technique requires little sample preparation and involves relatively simple data processing. The system described in this research is based on compact fan-beam type tomography, using a tungsten-anode x-ray tube with a relatively small focal spot (approximately 5 microm), coupled with a high-resolution x-ray detector system (approximately 10 microm). To produce a real 3-D data set using microcomputed tomography, x-ray projection views were acquired at 720 equally spaced angular positions (0.5 degrees) around the object of interest. These groups of views were then used to reconstruct a computed tomography image. A test grid with orthogonal test lines was used to calculate bone volume and bone surface. From these calculations, parallel plate equations were used to derive trabecular architectural parameters such as average trabecular plate thickness and average trabecular plate separation. Three-dimensional microarchitecture was evaluated using specialized stereological analysis software. Significant relationships between apparent bone mineral density (mg/cc) and 3-D structure were observed in femoral specimens from 66-wk-old Single Comb White Leghorn hens.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.290
Teacher spread0.255 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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