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Record W2407665464 · doi:10.1017/cbo9781139939751

Integrative Mechanobiology

2015· book· en· W2407665464 on OpenAlexaff
Yu Sun, Eddie Y. Chung, Niels Volkmann, Yue Shao, Christopher Moraes, Koichiro Uto, Murat Güvendiren, Jiandong Wu, Teresa Zulueta-Coarasa, Ge Yang, Yasaman Nematbakhsh, Mitsuhiro Ebara, Andrew W. Holle, Kyle G. Battiston, Chao Liu, Vincent F. Fiore, Andrea Leonard, Hye Mi Kim, Yoojin Shin, Jerel Mueller

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMechanobiologyNanotechnologyComputer scienceNanotopographyStem cell biologyEngineeringBiologyMaterials scienceCell biology

Abstract

fetched live from OpenAlex

The first of its kind, this comprehensive resource integrates cellular mechanobiology with micro-nano techniques to provide unrivalled in-depth coverage of the field, including state-of-the-art methods, recent advances, and biological discoveries. Structured in two parts, the first part offers detailed analysis of innovative micro-nano techniques including FRET imaging, electron cryo-microscopy, micropost arrays, nanotopography devices, laser ablation, and computational image analysis. The second part of the book provides valuable insights into the most recent technological advances and discoveries in areas such as stem cell, heart, bone, brain, tumor, and fibroblast mechanobiology. Written by a team of leading experts and well-recognised researchers, this is an essential resource for students and researchers in biomedical engineering.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0470.027

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 head0.026
GPT teacher head0.228
Teacher spread0.203 · 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 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

Citations10
Published2015
Admission routes1
Has abstractyes

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