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Record W2917837043 · doi:10.4012/dmj.2018-142

Gradual dehydration affects the mechanical properties and bonding outcome of adhesives to dentin

2019· article· en· W2917837043 on OpenAlex
Abu Faem Mohammad Almas Chowdhury, Pipop Saikaew, Mariko Matsumoto, Hidehiko Sano, Ricardo M. Carvalho

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

VenueDental Materials Journal · 2019
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of British Columbia
FundersHokkaido University
KeywordsDentinMaterials scienceAdhesiveCrossheadMolarBond strengthComposite materialDehydrationDentistryFlexural strengthChemistry

Abstract

fetched live from OpenAlex

This study evaluated the effects of dehydration on the mechanical properties of adhesive resins and dentin, and on the microtensile bond strength (µTBS) of adhesives. Third molars were randomly bonded with Clearfil Mega Bond (MB) or Clearfil SE Bond 2 (SE). After water-storage (37°C; 24 h), µTBS was obtained in 'wet' (tested after 5 min of removal from storage) and 'dehydrated' (tested after 10, 15 min and 24 h) conditions by a universal tester (crosshead speed: 1 mm/min). Data were analyzed by two-way ANOVA and Duncan's test. Hardness (H), Elastic modulus (E) and weight-loss of dentin beams and adhesive-resin discs were also monitored over time and analyzed by one-way repeated measures ANOVA and Bonferroni's test (α=0.05). Significant differences in bond strength were observed for adhesives and for conditions. Except for dentin's E, dehydration caused significant gradual changes in the H, E and weight of adhesive resins and dentin (p<0.05).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.081
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.035
GPT teacher head0.279
Teacher spread0.243 · 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