{"id":"W2036117720","doi":"10.1016/j.chb.2004.10.042","title":"The development of an instrument to measure the degree of animation predisposition of agent users","year":2004,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Digital Games and Media","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Operationalization; Animation; Construct (python library); Personalization; Trait; Computer science; Perception; Psychology; Human–computer interaction; Genetic predisposition; Social psychology; World Wide Web; Medicine; Computer graphics (images)","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":[0.0004430571,0.00005080756,0.00008716634,0.00004199965,0.0000910619,0.00002046896,0.000272791,0.00002671976,0.00000211012],"category_scores_gemma":[0.00001283371,0.00003500572,0.00003021679,0.0001295883,0.0001484607,0.00009056902,0.00004467491,0.00003599004,3.81893e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248816,"about_ca_system_score_gemma":0.0001050254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009943829,"about_ca_topic_score_gemma":0.002587286,"domain_scores_codex":[0.9991514,0.00004459818,0.0002781248,0.00008310306,0.0003355201,0.0001072538],"domain_scores_gemma":[0.9996199,0.00002052718,0.0001178767,0.0001279104,0.00007072686,0.00004302853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002432844,0.000447756,0.00628243,0.0000200596,0.00001134423,9.713044e-7,0.1291349,0.0004083889,0.003942137,0.0107065,0.00001945044,0.8490017],"study_design_scores_gemma":[0.0006633187,0.0003711597,0.9709502,0.0003113471,0.00003052551,4.00769e-7,0.01061946,0.00001242457,0.01475732,0.0003873533,0.00175977,0.0001366637],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99771,0.00001130473,0.0004592263,0.0000872792,0.000181162,0.000445547,0.00000235846,0.000006501138,0.00109658],"genre_scores_gemma":[0.9965488,0.000001855542,0.00336025,0.00001105335,0.00001683347,0.00002665116,0.000003913012,0.00000332226,0.00002732183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9646679,"threshold_uncertainty_score":0.1443766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09139209260254913,"score_gpt":0.3195113029067863,"score_spread":0.2281192103042372,"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."}}