{"id":"W4366547999","doi":"10.1145/3544549.3573794","title":"GenAICHI 2023: Generative AI and HCI at CHI 2023","year":2023,"lang":"en","type":"article","venue":"","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Generative grammar; Computer science; Generative Design; Generative model; Human–computer interaction; Cognitive science; Artificial intelligence; Psychology; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001419547,0.0000918756,0.00009388674,0.0001045413,0.0001902968,0.00003599374,0.00009817983,0.00004618482,0.008094689],"category_scores_gemma":[0.00005968985,0.00007892693,0.0000256255,0.0004720118,0.0001288472,0.00006000703,0.00008820483,0.0000890501,0.00575187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001741528,"about_ca_system_score_gemma":0.00002093233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008952343,"about_ca_topic_score_gemma":0.000181159,"domain_scores_codex":[0.9991375,0.00007569585,0.0001061291,0.0003410567,0.0001100994,0.0002295824],"domain_scores_gemma":[0.9995182,0.0001014643,0.00002718487,0.0002289646,0.0000237388,0.0001004878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001544553,0.00008395492,0.01467289,0.00000349276,0.00001273479,0.00001505895,0.003174387,0.000003758959,0.01404397,0.009983816,0.9540907,0.003899862],"study_design_scores_gemma":[0.0006560536,0.0001746588,0.6887154,0.000005866422,0.00001430075,0.0000705517,0.002209267,0.001156875,0.006556435,0.0007081237,0.2993507,0.0003817342],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.8423775,0.00006635776,0.0001223343,0.008424186,0.00172377,0.0001465502,0.00002223378,0.0001285975,0.1469885],"genre_scores_gemma":[0.4744404,0.00007073553,0.00007895275,0.004440843,0.000161213,0.00004588565,0.00001424479,0.00001056441,0.5207372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6740425,"threshold_uncertainty_score":0.9950223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05438084628928203,"score_gpt":0.3957586629603024,"score_spread":0.3413778166710204,"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."}}