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Record W7100647078

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2016· article· en· W7100647078 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCybersecurity and Information Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaIndigenousDiseasePopulationEpidemiology
DOInot available

Abstract

fetched live from OpenAlex

most common types of dementia were Alzheimer’s dementia (44%) and mixed dementia diagnoses enous Canadian sample, and rates of Alzheimer’s-type other populations worldwide [3]. These remote Aboriginal Alzheimer’s & DementiaFourth World Indigenous people who have well-documented differences in life expectancy, midlife disease rates, and levels of adverse social and biological risk factors compared with their First World neighbors [1]. dementia were relatively low in the Indigenous group [2]. In contrast, another study found a high prevalence of dementia in a remote Aboriginal Australian population relative to the nonindigenous Australian rate and mostThere are little data on dementia rates in the major(29%). Estimated prevalence of MCI was 17.7%. Conclusion: Consistent with previous findings in a remote population, urban and regional Aboriginal Australians face high rates of dementia at younger ages, most commonly Alzheimer’s dementia.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.604
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3960.200

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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