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Record W2600008022 · doi:10.1002/adem.201600727

Nano‐Hydroxyapatite and TiO<sub>2</sub> Bioactivated Polymer for Implant Applications

2017· article· en· W2600008022 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Engineering Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceApatitePolymerPorosityNano-ImplantChemical engineeringImmersion (mathematics)NanotechnologyComposite material

Abstract

fetched live from OpenAlex

Polymers have been successfully used for implant applications, however, challenges remain as their design requires a delicate balance between mechanical, chemical, and physical properties to ensure cell survival and tissue formation. Additive manufacturing techniques, such as SLA, offer the opportunity to achieve desired physical and mechanical properties because of the precision and control over architecture. Such control allows manipulation over the distribution of mechanical properties throughout the implant. PMA/HA and PMA/TiO 2 solid and porous structures are, therefore, manufactured using room temperature SLA techniques. HA and TiO 2 are added to the polymer for bio‐functionalizing purposes. The apatite forming ability of the samples are evaluated using HBSS immersion. All PMA and PMA/TiO 2 samples does not show any bioactivity in terms of apatite formation, although limited amounts of CaO is found on PMA surfaces. PMA/HA samples demonstrate bioactivity with newly formed apatite formation observed after 3 and 5 week HBSS immersion.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.210
Teacher spread0.205 · 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