Cohort Profiles: Personality Measurements at the Estonian Biobank of the Estonian Genome Center, University of Tartu
Bibliographic record
Abstract
Like all behaviour, personality traits are substantially heritable, but their genetic background is poorly understood. Investigating traits’ genetic background could help explain disparities in health and other life outcomes they contribute to. We describe two cohorts of the Estonian Biobank for whom, besides self- and informant-rated personality traits, detailed data are available on a wide range of measures including health behaviour, biomarkers, anthropometric measurements, and medical diagnoses and treatments. The first cohort ( N self-report = 3,640, N informant-report = 3,488) filled out the NEO Personality Inventory-3 (NEO-PI-3) between 2008 and 2018. The second cohort ( N self-report = 77,400, N informant-report = 21,986), collected between 2021 and 2022, responded to a large and diverse item pool called the 100 Nuances of Personality (100NP) covering the Big Five and other traits. Research opportunities include investigation of personality traits’ properties, gene discovery, prediction of health and well-being, and causal modelling. New data are added periodically through additional data collection waves and linkage with various registries and databases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".