{"id":"W4250284255","doi":"10.31234/osf.io/894zt","title":"Dyadic and triadic search: Benefits, costs, and predictors of group performance","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Task (project management); Set (abstract data type); Visual search; Gaze; Variance (accounting); Scale (ratio); Psychology; Group (periodic table); Cognitive psychology; Computer science; Social psychology; Artificial intelligence; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002746436,0.0005123292,0.0007622917,0.0008255363,0.0006211107,0.001414644,0.0004350543,0.0009368227,0.004286724],"category_scores_gemma":[0.02312359,0.0001976366,0.0004652062,0.0007419916,0.000720509,0.001470286,0.00182683,0.0009026036,0.0005789433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003478799,"about_ca_system_score_gemma":0.0003314761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608136,"about_ca_topic_score_gemma":0.002866258,"domain_scores_codex":[0.9984587,0.0006486968,0.0001186869,0.0003175232,0.0002917424,0.0001646899],"domain_scores_gemma":[0.9770995,0.01350048,0.003876689,0.002123466,0.0007883743,0.00261158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001975824,0.0006071372,0.9601706,0.0001639235,0.0005257184,0.0002461435,0.002760494,0.003335916,0.003364969,0.0007842503,0.0006708819,0.02539429],"study_design_scores_gemma":[0.00002615298,0.0004794915,0.9890094,0.00002424647,0.00008621908,0.0001840242,0.001305599,0.006744383,0.0002824888,0.001483327,0.0003473059,0.00002734488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977844,0.0001923169,0.0008772057,0.00006434086,0.000009182218,0.00001365955,0.00006805297,0.0000119744,0.0009789361],"genre_scores_gemma":[0.9989279,0.00004697148,0.000614579,0.000008742445,0.000008010914,0.00002127688,0.00008573961,0.00001009435,0.0002767458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004286724,"threshold_uncertainty_score":0.0145247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03852607505788484,"score_gpt":0.2799432388993827,"score_spread":0.2414171638414979,"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."}}