{"id":"W2951662761","doi":"10.48550/arxiv.cs/0212015","title":"Answering Subcognitive Turing Test Questions: A Reply to French","year":2002,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Turing test; Turing; Embodied cognition; Computer science; Scope (computer science); Question answering; Test (biology); Cognitive science; World Wide Web; Epistemology; Information retrieval; Artificial intelligence; Data science; Psychology; Philosophy; Programming language","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":[0.0003503194,0.0002132987,0.0002292905,0.00008479063,0.0006100712,0.0001305279,0.0003617918,0.0002390072,0.000293173],"category_scores_gemma":[0.0017516,0.0002059953,0.0001294469,0.0002928391,0.000110927,0.0002259274,0.0003026258,0.0004900511,0.0005385642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002616643,"about_ca_system_score_gemma":0.0001710404,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04104545,"about_ca_topic_score_gemma":0.006336993,"domain_scores_codex":[0.9983156,0.0001311918,0.0002642381,0.0005521661,0.0003173751,0.0004194075],"domain_scores_gemma":[0.9989955,0.0001149817,0.000131073,0.0003144837,0.0002499379,0.0001940384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001729348,0.0005534916,0.6919312,0.0002540297,0.0002077712,0.0003059083,0.2584752,0.0003252483,0.004758565,0.001500465,0.01698367,0.02468722],"study_design_scores_gemma":[0.0004061131,0.0001113092,0.8773383,0.001547567,0.0001248657,0.000008295759,0.01876225,0.00006009984,0.001611559,0.002131732,0.09643381,0.001464051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600294,0.00115099,0.0001788672,0.003734338,0.0006368904,0.0005494264,0.00002801231,0.0003434674,0.03334858],"genre_scores_gemma":[0.9877213,0.0002711937,0.0003796839,0.0008437066,0.001139503,0.0001217024,0.00002409937,0.00001928111,0.009479486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2397129,"threshold_uncertainty_score":0.9653403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05119326442578204,"score_gpt":0.3137852076361127,"score_spread":0.2625919432103307,"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."}}