{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005779644,0.0003748994,0.0003421807,0.001804579,0.0004704915,0.0008163386,0.0004509591,0.0007895559,0.001572341],"category_scores_gemma":[0.02287786,0.0002245435,0.0005244993,0.00075519,0.0003549207,0.0007325516,0.0006979093,0.0009447873,0.0005794421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004125672,"about_ca_system_score_gemma":0.0009998274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009165145,"about_ca_topic_score_gemma":0.001346475,"domain_scores_codex":[0.9970914,0.001668894,0.0002756493,0.0001967041,0.0005944049,0.0001729249],"domain_scores_gemma":[0.9765587,0.01584943,0.00202039,0.001111449,0.003642849,0.0008172173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009085783,0.003437478,0.7210044,0.000266726,0.0001382393,0.0001038638,0.004336029,0.00174765,0.02393197,0.005349017,0.002163725,0.2366123],"study_design_scores_gemma":[0.0001764,0.005064175,0.9265447,0.0001195412,0.0001876145,0.0004011814,0.002199651,0.0271266,0.02519501,0.003673504,0.009192947,0.0001186423],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9297451,0.0001647791,0.05555925,0.0003446074,0.0001464196,0.00271023,0.0006406747,0.0002938346,0.01039517],"genre_scores_gemma":[0.9116126,0.0001442807,0.08229102,0.0001969558,0.00004679181,0.002929608,0.000636097,0.00002970034,0.002112906],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005779644,"threshold_uncertainty_score":0.03056604,"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."}}