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Record W2085460035 · doi:10.2320/matertrans.46.1633

Hydroxyapatite Coatings on a 3D Porous Surface Using Thermal Substrate Method

2005· article· en· W2085460035 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

VenueMATERIALS TRANSACTIONS · 2005
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of Science
KeywordsMaterials scienceCoatingSubstrate (aquarium)PorositySinteringTitaniumAqueous solutionChemical engineeringComposite materialBase (topology)Metallurgy

Abstract

fetched live from OpenAlex

Hydroxyapatite (HAp) coated films were obtained using a thermal substrate method, whereby an aqueous solution at pH=7 or 8 containing Ca2+ and PO43− ions was used to deposit a coating on porous-surfaces substrates formed by sintering 44–150 μm-sized Ti6Al4V powders on a solid commercially pure titanium (cpTi) base. The HAp coating conditions used were: coating time = ∼900 s and substrate temperature equal to 373 or 393 K. Coatings formed in a pH=7 solution at 373 K for 900 s and in a pH=8 solution at 393 K for 300 s showed that all of the surfaces of the Ti6Al4V sintered particles (both front and back faces) and the base cpTi substrate were covered with HAp, and that they maintained their original open-pored geometry. The precipitated HAp appeared to be denser on porous-surfaced samples compared with plain-surfaced cpTi samples. Use of the thermal substrate method with appropriate pH control, and substrate heating temperature and time was effective for forming HAp coatings on powder-sintered samples with a complex topography.

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), Insufficient payload (model declined to judge)
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 score0.445
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)0.0020.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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