MétaCan
Menu
Back to cohort

Structure and Composition Comparison of Bone Mineral and Apatite Layers Formed in Vitro

2000· article· en· W2046958699 on OpenAlexaff
Ling Hong Guo, Mei Chuan Huang, Yang Leng, J. E. Davies, Xing Dong Zhang

Bibliographic record

VenueKey engineering materials · 2000
Typearticle
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsApatiteComposition (language)MineralMaterials scienceMineralogyIn vitroGeochemistryGeologyChemistryMetallurgyBiochemistryArt

Abstract

fetched live from OpenAlex

In this in vitro study, the apatite formed on hydroxyapatite (HA) and alpha-tricalcium phosphates (α - TCP), as well as human trabecular bone were characterized with thin film X-ray diffraction (TF-XRD) and Fourier Transform Infrared (FTIR), as well as Scanning Electron Microscopy-Energy Dispersive X-ray spectra (SEM-EDX) to compare their structure and composition. The morphology and chemical compositions of the apatites formed on the HA and α - TCP exhibited a great difference. The Ca/P molar ratio of the apatite formed on the α - TCP is 1.5 close to bone, but the Ca/P molar ratio of the apatite formed on HA is 1.1. The Rietveld refinement showed that HA structure can be used to simulate the structure of apatites formed and bones mineral. The crystal size and microstrain of apatites and bone mineral exhibited great difference. These results indicated that the phase of calcium phosphate ceramics have certain effects on the composition and morphology of apatite in vitro. From the structure view, the apatites formed on HA and α - TCP is similar with bone minerals.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 designBench or experimental
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

Citations11
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueKey engineering materialsSame topicEngineering Technology and MethodologiesFrench-language works237,207