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Record W2165629175 · doi:10.1093/imamat/hxu059

Erratum to: Poroelastic Materials Reinforced by Statistically Oriented Fibres - Numerical Implementation and Application to Articular Cartilage

2014· erratum· en· W2165629175 on OpenAlexaff
Salvatore Federico, Alfio Grillo

Bibliographic record

VenueIMA Journal of Applied Mathematics · 2014
Typeerratum
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPoromechanicsArticular cartilageMaterials scienceComposite materialBiomedical engineeringEngineeringMedicinePathologyOsteoarthritisPorosityPorous medium

Abstract

fetched live from OpenAlex

Erratum to: Poroelastic Materials Reinforced by Statistically Oriented Fibres - Numerical Implementation and Application to Articular Cartilage IMA J. Appl. Math., 79 (5): 1027–1059, 2014 DOI: 10.1093/imamat/hxu039 In the Introduction and Discussion sections of our paper (Tomic et al., 2014), we have erroneously classified the permeability model by Pierce et al. (2013a) among those in which the effect of the collagen fibres has not been taken into account. In fact, Pierce et al. (2013a, b) do take the effect of the collagen distribution into account, and this has unfortunately escaped our attention. The model by Pierce et al. (2013a, b) accounts for the fibre distribution, by introducing a term depending on the referential fibre direction (⁠|${\boldsymbol{{{a}}}}_0$| in their notation, |${\boldsymbol{{{M}}}}$| in ours), that has the effect of decreasing the permeability in the direction orthogonal to each fibre. The model that we employed in our work (Tomic et al., 2014) is different from that by Pierce et al. (2013a, b) in that it employs an upscaling approach, based on the consideration of a reference element of volume (REV), as described by Federico and Herzog (2008a, b) for the case of small deformations, and by Federico and Grillo (2012) for the case of large deformations. Although the large deformation model by Federico and Grillo (2012) was conceived with articular cartilage in mind, it was mainly devoted to elucidating the whole mathematical framework. Indeed, its first full implementation into a cartilage model with realistic histological information came only with our later work by Tomic et al. (2014). Therefore, the further statement we made at the end of the Abstract, i.e., This could be regarded as the first full, realistic model of articular cartilage in which the effect of the statistically oriented collagen fibres was accounted for not only for the elastic properties, but also for the permeability. We propose a full, histologically realistic model of articular cartilage in which the effect of the statistically oriented collagen fibres was accounted not only for the elastic properties, but also for the permeability, and which, for the first time, is based on the accurate upscaling procedures from the fibre to the tissue level that we had previously developed (Federico and Herzog, 2008a, b; Federico and Grillo, 2012).

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)0.001
metaresearch head score (Gemma)0.019
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0320.016

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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

Citations47
Published2014
Admission routes1
Has abstractno

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