{"id":"W3123195249","doi":"","title":"Contrasting Rule-Based and Similarity-Based Category Learning: The Effects of Mood and Prior Knowledge on Ambiguous Categorization","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; York University","funders":"","keywords":"Categorization; Similarity (geometry); Product (mathematics); Feature (linguistics); Product category; Concept learning; Mood; Phenomenon; Cognitive psychology; Psychology; Artificial intelligence; Computer science; Natural language processing; Social psychology; Mathematics; Linguistics; Epistemology","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":[0.0003301442,0.0001568915,0.0002116917,0.0001362682,0.0001827372,0.00004793282,0.0002852037,0.00006754233,0.000002774512],"category_scores_gemma":[0.0002774744,0.0001105969,0.00003922788,0.0002891326,0.0001290276,0.0001662116,0.00008815831,0.0001765869,0.000001381772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002046,"about_ca_system_score_gemma":0.00005573948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009180542,"about_ca_topic_score_gemma":0.00003232721,"domain_scores_codex":[0.9989693,0.0001729881,0.0002049338,0.0003305847,0.0001411254,0.0001810246],"domain_scores_gemma":[0.9988598,0.000478771,0.0001613925,0.0003183512,0.0001216447,0.00006009987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001875586,0.001512922,0.05019552,0.001179291,0.0002013931,0.00002940166,0.007865846,0.001012664,0.08239201,0.1842142,0.00008915797,0.67112],"study_design_scores_gemma":[0.001372233,0.000991783,0.05031076,0.000099551,0.0000836611,0.000002785382,0.00004981037,0.3377952,0.6005746,0.008278167,0.00006628819,0.0003752304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1055775,0.0001168744,0.8915874,0.00008531052,0.00003265073,0.000282639,1.995306e-7,0.0002609111,0.002056604],"genre_scores_gemma":[0.9758892,0.000007606873,0.02388951,0.0001142072,0.000009573626,0.00002244055,0.000001660572,0.00001127284,0.00005450557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8703118,"threshold_uncertainty_score":0.4510013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358512611417463,"score_gpt":0.2411206950493141,"score_spread":0.2275355689351395,"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."}}