{"id":"W2082209656","doi":"10.1145/634067.634293","title":"An analysis of the influence of need for cognition on dynamic queries usage","year":2001,"lang":"en","type":"article","venue":"","topic":"Software Engineering and Design Patterns","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Construct (python library); Computer science; Cognition; Personality; Trait; Need for cognition; Correlation; Big Five personality traits; Data science; Cognitive psychology; Information retrieval; Psychology; Social psychology; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000191264,0.00003349238,0.00008618534,0.00007498033,0.00006516211,0.000007387046,0.000104383,0.00002943541,0.0000355176],"category_scores_gemma":[0.0001788367,0.00002407472,0.00006359812,0.0004745189,0.00007703294,0.00006519575,0.000003585176,0.00001873765,4.907188e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348204,"about_ca_system_score_gemma":0.00001926345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001494823,"about_ca_topic_score_gemma":0.002175594,"domain_scores_codex":[0.9996033,0.00003720334,0.00009150394,0.00006246041,0.0001315914,0.00007391064],"domain_scores_gemma":[0.9995543,0.0002049542,0.00004471841,0.0001058871,0.00007043425,0.00001965759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001090573,0.0002455096,0.7349676,0.00003728201,0.0004383859,6.794623e-7,0.01803607,0.2059962,0.01816952,0.01666166,0.00006355115,0.005274513],"study_design_scores_gemma":[0.000161386,0.0001191989,0.9851738,0.00002541201,0.0002769458,7.489601e-8,0.0028881,0.008747227,0.001457541,0.0008769076,0.0001769019,0.00009652926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966273,0.00000270974,0.03289514,0.00005160212,0.00002759271,0.00009377099,0.00001263573,0.00003215884,0.0006113844],"genre_scores_gemma":[0.9994857,0.000006262761,0.0002424804,0.0000568086,0.000007343181,0.000007132781,0.000003256504,0.000002348021,0.0001886172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2502061,"threshold_uncertainty_score":0.2259736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01513718851910426,"score_gpt":0.3070661364025756,"score_spread":0.2919289478834713,"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."}}