{"id":"W7137223280","doi":"10.1145/3772673.3772679","title":"Perceptlets: Key to Machine Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Key (lock); Matching (statistics); Representation (politics); Automaton; External Data Representation; Applications of artificial intelligence; Knowledge representation and reasoning; Component (thermodynamics)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008567917,0.000264889,0.0002705488,0.0005398146,0.0007258484,0.0007987117,0.00137384,0.00007675271,0.001609004],"category_scores_gemma":[0.0005130945,0.0002512669,0.0001335779,0.002846877,0.0001110398,0.0006634351,0.001835798,0.000407025,0.002102735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000976431,"about_ca_system_score_gemma":0.0003196766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002383723,"about_ca_topic_score_gemma":0.0001449164,"domain_scores_codex":[0.997326,0.0001455052,0.0003681423,0.001014663,0.0004092624,0.0007363896],"domain_scores_gemma":[0.9987382,0.0001509238,0.0000525205,0.0005023695,0.0002889318,0.0002670552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001016355,0.00008234776,0.002936144,0.00003422109,0.00002660754,0.00001931458,0.005077272,0.0002624785,0.002970592,0.05952104,0.006971852,0.922088],"study_design_scores_gemma":[0.0004518021,0.0002967179,0.01029547,0.0003875728,0.00002373808,0.000008688833,0.001154855,0.3478402,0.001479436,0.001852496,0.6356056,0.0006033964],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003354586,0.0002800887,0.7173864,0.01586592,0.001103105,0.0002588395,0.000001268729,0.0001487274,0.2616011],"genre_scores_gemma":[0.8117136,0.00009857504,0.0139005,0.01430673,0.00008260257,0.00001390496,0.000001269372,0.000006761373,0.159876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9214846,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169149007216186,"score_gpt":0.2780483119956376,"score_spread":0.261133411274019,"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."}}