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Wolff's law (bone functional adaptation)

2018· other· en· W2937070602 on OpenAlexaff
Jay T. Stock

Bibliographic record

VenueThe International Encyclopedia of Biological Anthropology · 2018
Typeother
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsWestern University
Fundersnot available
KeywordsBone remodelingAdaptation (eye)Rigidity (electromagnetism)Bone tissueBone resorptionAnatomyMaterials scienceMedicineBiologyNeuroscienceComposite materialInternal medicine

Abstract

fetched live from OpenAlex

Wolff's law was proposed in 1892 by Julius Wolff, a German anatomist and surgeon, as a mathematical law that described the response of bone to mechanical loading. The mathematical aspects of his law do not fully describe the process of mechanically induced bone remodeling; however, the general process, often called bone functional adaptation, is well supported by experimental and comparative studies. Increases in the loading of living bone tissue are known to generate deposition of new bone, which increase mechanical rigidity. Similarly, decreases in mechanical loading, particularly associated with prolonged bed rest or weightlessness, lead to adaptive resorption of bone tissue and a decrease in mechanical rigidity. The basic principles of bone functional adaptation, which stem from Wolff's law, have led to research that demonstrates the existence of differences in the mechanical properties of bone, namely differences between species and associated with locomotion and posture; differences within species and associated with habitual activity and cultural change in the past; and differences among modern humans and associated with patterns of habitual activity during life.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0890.001

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 head0.055
GPT teacher head0.346
Teacher spread0.291 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations27
Published2018
Admission routes1
Has abstractyes

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