{"id":"W3186953399","doi":"","title":"Modelling Recognition in Human Puzzle Solving","year":2021,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Adversarial system; Artificial intelligence; Simple (philosophy); Context (archaeology); Reinforcement learning; Artificial neural network; Machine learning; Epistemology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005772747,0.0004048539,0.0004007255,0.0003769064,0.0003142357,0.001182835,0.0008415182,0.001290955,0.002590572],"category_scores_gemma":[0.004695394,0.0004338293,0.0005806366,0.0003614327,0.001750928,0.001931459,0.0008837504,0.001148475,0.0004246348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044041,"about_ca_system_score_gemma":0.0009850078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01361066,"about_ca_topic_score_gemma":0.008330364,"domain_scores_codex":[0.9996713,0.0001409081,0.0000173094,0.00008745653,0.00004425383,0.00003887379],"domain_scores_gemma":[0.9988288,0.0007249204,0.0001695518,0.0001105453,0.0000889013,0.00007723761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000588721,0.00005145818,0.002677236,0.00005979133,0.00002874686,0.000149258,0.0003848375,0.8948479,0.001954427,0.0862271,0.000399162,0.01316108],"study_design_scores_gemma":[0.00001114286,0.00002341648,0.000546394,0.000006934802,0.000006485273,0.0000331598,0.0000281909,0.9321956,0.0002818309,0.06631304,0.000543722,0.00001020772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3625345,0.0009093288,0.6066834,0.002147959,0.00007415502,0.0001002374,0.0002271462,0.0004913153,0.02683196],"genre_scores_gemma":[0.9588832,0.0002613576,0.03668509,0.00006761424,0.0000155144,0.00007255859,0.00007695493,0.00002883964,0.003908895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01361066,"threshold_uncertainty_score":0.02706289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0475140153750972,"score_gpt":0.2508875830646463,"score_spread":0.2033735676895491,"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."}}