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Record W2800184149 · doi:10.1126/science.aao2189

Fractal-like hierarchical organization of bone begins at the nanoscale

2018· article· en· W2800184149 on OpenAlexafffund
Natalie Reznikov, Matthew Bilton, Leonardo Lari, Molly M. Stevens

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsSimon Fraser University
FundersFP7 Nanosciences, Nanotechnologies, Materials and new Production TechnologiesNuclear PhysicsBritish Columbia Knowledge Development FundEngineering and Physical Sciences Research CouncilWellcomeImperial College LondonImperial College Healthcare NHS TrustMcGill UniversitySimon Fraser UniversityWeizmann Institute of ScienceWellcome TrustWestern Economic Diversification Canada
KeywordsFractalMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

The components of bone assemble hierarchically to provide stiffness and toughness. However, the organization and relationship between bone's principal components-mineral and collagen-has not been clearly elucidated. Using three-dimensional electron tomography imaging and high-resolution two-dimensional electron microscopy, we demonstrate that bone mineral is hierarchically assembled beginning at the nanoscale: Needle-shaped mineral units merge laterally to form platelets, and these are further organized into stacks of roughly parallel platelets. These stacks coalesce into aggregates that exceed the lateral dimensions of the collagen fibrils and span adjacent fibrils as continuous, cross-fibrillar mineralization. On the basis of these observations, we present a structural model of hierarchy and continuity for the mineral phase, which contributes to the structural integrity of bone.

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 score0.001
Threshold uncertainty score0.002

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations644
Published2018
Admission routes2
Has abstractyes

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