{"id":"W4236144548","doi":"10.2196/preprints.34567","title":"Cocreating a Harmonized Living Lab for Big Data–Driven Hybrid Persona Development: Protocol for Cocreating, Testing, and Seeking Consensus (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Innovative Approaches in Technology and Social Development","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Living lab; Protocol (science); Context (archaeology); Preprint; Health care; Independent living; Workflow; Knowledge management; Computer science; Data science; World Wide Web; Medicine; Political science; Gerontology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002903255,0.0007735233,0.000992753,0.0003931081,0.001490758,0.001078844,0.001045302,0.0004835308,0.00005300638],"category_scores_gemma":[0.009047363,0.0008029427,0.0001228227,0.0004434563,0.0002937984,0.0002947265,0.007937701,0.000797166,0.000005990034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991002,"about_ca_system_score_gemma":0.0007481078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002625637,"about_ca_topic_score_gemma":0.0001132431,"domain_scores_codex":[0.9957203,0.00005252756,0.001180307,0.001871961,0.0003493991,0.0008255098],"domain_scores_gemma":[0.9949225,0.001308889,0.001409469,0.0008495476,0.001480396,0.00002918326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001312966,0.001723555,0.3336592,0.05897911,0.004746319,0.0001553604,0.008665758,0.0003060294,0.003317091,0.02149218,0.02276653,0.5428759],"study_design_scores_gemma":[0.01717593,0.000138655,0.0322153,0.02555118,0.001160948,0.0001057909,0.02548402,0.6198222,0.006701987,0.01962492,0.2418522,0.01016686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"methods","genre_scores_codex":[0.2393187,0.00009544235,0.2622378,0.001482065,0.002005195,0.4610649,0.0001800875,0.003108689,0.03050707],"genre_scores_gemma":[0.1481574,0.000001720327,0.504357,0.0009496479,0.001734245,0.3428976,0.000782708,0.0001934503,0.0009262385],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.6195161,"threshold_uncertainty_score":0.9999582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1722897563708483,"score_gpt":0.3174523689308212,"score_spread":0.1451626125599729,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}