{"id":"W7101725183","doi":"10.1109/mc.2025.3596552","title":"IEEE 3152: A Standard for Human and Machine Agency Identification","year":2025,"lang":"","type":"article","venue":"Computer","topic":"Chemical synthesis and alkaloids","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Identification (biology); Agency (philosophy); Key (lock); Expert system","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"],"consensus_categories":[],"category_scores_codex":[0.0001905777,0.0002549718,0.0003569659,0.00005093363,0.0002980645,0.0002509237,0.0002717495,0.0001976009,0.0004711815],"category_scores_gemma":[0.00002529504,0.0002603737,0.0001807834,0.0001104043,0.0001080567,0.00008980823,0.0001527943,0.0001762367,0.00001035165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008507363,"about_ca_system_score_gemma":0.00003597179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007144398,"about_ca_topic_score_gemma":0.000004515131,"domain_scores_codex":[0.9984189,0.00001829273,0.0005342023,0.0005936665,0.0001410045,0.0002939698],"domain_scores_gemma":[0.9990292,0.0001793093,0.0001345633,0.0004468081,0.0001098685,0.0001002874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001132189,0.0001872871,0.0005105296,0.001367444,0.0001926033,0.000002584345,0.0001779486,0.000003387876,0.5549589,0.002481501,0.0278815,0.4121231],"study_design_scores_gemma":[0.001870748,0.00008090203,0.001423626,0.0005727398,0.0002433165,0.000002339319,0.00001024121,0.008440915,0.8425591,0.01592462,0.1283762,0.0004951779],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7197138,0.004222701,0.2687466,0.001431481,0.001101783,0.0005195345,0.0003577604,0.0001037099,0.003802661],"genre_scores_gemma":[0.9882853,0.0001237263,0.001497075,0.0001979971,0.0006968207,0.00004368721,0.00006146774,0.0000250166,0.009068903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4116279,"threshold_uncertainty_score":0.9999849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872529427791546,"score_gpt":0.281049193639427,"score_spread":0.2623238993615116,"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."}}