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Record W2157253302 · doi:10.1017/s0714980809990055

The Canadian Longitudinal Study on Aging (CLSA)

2009· article· fr· W2157253302 on OpenAlexafffundabout
Parminder Raina, Christina Wolfson, Susan Kirkland, Lauren E. Griffith, Mark Oremus, Christopher Patterson, Holly Tuokko, Margaret J. Penning, Cynthia Balion, David B. Hogan, Andrew Wister, Hélène Payette, Harry S. Shannon, Kevin Brazil

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2009
Typearticle
Languagefr
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsSimon Fraser UniversityHamilton Regional Laboratory Medicine ProgramDalhousie UniversityUniversity of CalgaryMcGill UniversityUniversity of VictoriaHealth and Social Services Centre University Institute of Geriatrics of SherbrookeMcGill University Health CentreMcMaster University
FundersCanadian Institutes of Health Research
KeywordsPsychosocialLongitudinal studyGerontologyAffect (linguistics)Longitudinal dataSuccessful agingHealthy agingPsychologyDonationMedicineDemographyPsychiatryPolitical sciencePathologySociology

Abstract

fetched live from OpenAlex

ABSTRACTCanadians are living longer, and older persons are making up a larger share of the population (14% in 2006, projected to rise to 20% by 2021). The Canadian Longitudinal Study on Aging (CLSA) is a national longitudinal study of adult development and aging that will recruit 50,000 Canadians aged 45 to 85 years of age and follow them for at least 20 years. All participants will provide a common set of information concerning many aspects of health and aging, and 30,000 will undergo an additional in-depth examination coupled with the donation of biological specimens (blood and urine). The CLSA will become a rich data source for the study of the complex interrelationship among the biological, physical, psychosocial, and societal factors that affect healthy aging.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.031
GPT teacher head0.272
Teacher spread0.242 · 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.

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

Citations626
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicNutritional Studies and DietFrench-language works237,207