{"id":"W4312843895","doi":"10.1609/aiide.v17i1.18886","title":"The Impact of Visualizing Design Gradients for Human Designers","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Set (abstract data type); Process (computing); Design process; Human–computer interaction; Engineering design process; Component (thermodynamics); Subject (documents); Software engineering; Data science; World Wide Web; Work in process; Engineering; Programming language; Operations management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02127288,0.001548812,0.0006107547,0.00212717,0.001260876,0.008172582,0.001380848,0.001719505,0.005887456],"category_scores_gemma":[0.1219191,0.0007694491,0.000529482,0.0009033302,0.002369908,0.006566644,0.00463176,0.001311353,0.000914398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426029,"about_ca_system_score_gemma":0.001440653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008260289,"about_ca_topic_score_gemma":0.001484299,"domain_scores_codex":[0.9764289,0.01835131,0.0007617496,0.00170704,0.002235407,0.0005155888],"domain_scores_gemma":[0.8800605,0.09902094,0.005142931,0.009885805,0.004222586,0.001667278],"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":[0.003278452,0.001436045,0.07618671,0.002381312,0.000167582,0.001207049,0.1292704,0.02768644,0.04540384,0.05902902,0.008406223,0.6455469],"study_design_scores_gemma":[0.002533546,0.01056631,0.07554808,0.003274516,0.0009509826,0.003311609,0.1138061,0.2423775,0.08573122,0.2046855,0.2562034,0.00101132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7110019,0.0008503837,0.247491,0.002289811,0.0001635612,0.0005175241,0.000174882,0.004564666,0.03294621],"genre_scores_gemma":[0.8967533,0.0001405387,0.09999269,0.0001897708,0.00001864327,0.0002032281,0.0001040415,0.0006408449,0.001956892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02127288,"threshold_uncertainty_score":0.1125031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09860487181776681,"score_gpt":0.3618763775400612,"score_spread":0.2632715057222944,"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."}}