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Record W2899250573 · doi:10.23937/2469-5734/1510061

Acidity and Dental Erosion from Apple- and Grape-Juice (An in vitro and in vivo Report)

2018· article· en· W2899250573 on OpenAlexafffund
Touyz Louis ZG, Leonardo M. Nassani

Bibliographic record

VenueInternational Journal of Oral and Dental Health · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsMcGill University
FundersMcGill University
KeywordsChemistryGrape seed extractFood scienceInductively coupled plasmaInductively coupled plasma atomic emission spectroscopyDentistryVolunteerMolarMedicineBiology

Abstract

fetched live from OpenAlex

First: Six commercially available potable apple and grape juices were measured (six times each drink) for pH and buffering using 0.5 Molar NaOH with a Mettler DL 25 Automatic Titrator. The apple and grape juices were measured separately, using a 50 mL bolus for measures, 6 times for each. Second: Two volunteer cohorts; (One fully dentate WITH TEETH (mean age 20, M:F 6:6, n = 12) the second edentulous WITHOUT TEETH (mean age 61, M:F 6:6, n = 12)), were used to swish with 50 mL aliquots of Apple and/or Grape juices for 30 seconds. Each sample was analyzed six times with Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES) for Calcium, and Phosphorous.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.706

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 head0.026
GPT teacher head0.352
Teacher spread0.326 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2018
Admission routes2
Has abstractyes

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