{"id":"W4293201266","doi":"10.1007/978-3-030-95346-1_12","title":"Transhumanist Technologies for the Transhumanist Consumer: An Abstract","year":2022,"lang":"en","type":"book-chapter","venue":"Developments in marketing science: proceedings of the Academy of Marketing Science","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Transhumanism; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006596473,0.0008119506,0.000416934,0.001138819,0.001176837,0.005605205,0.0008489414,0.002803641,0.01572694],"category_scores_gemma":[0.0005191,0.0002656471,0.0003683457,0.001141232,0.004765042,0.007100347,0.001909782,0.003286158,0.004290434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002027962,"about_ca_system_score_gemma":0.001138018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941299,"about_ca_topic_score_gemma":0.003640757,"domain_scores_codex":[0.9997334,0.00008377711,0.000007493009,0.00004714818,0.0001028849,0.00002530572],"domain_scores_gemma":[0.9996988,0.0001876764,0.0000100028,0.00002880604,0.00005243661,0.00002239295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001580467,0.00005290896,0.00005532905,0.000358396,0.000005615425,0.0001428744,0.001463025,0.0002665729,0.0007575401,0.87761,0.05000218,0.06926974],"study_design_scores_gemma":[0.000003865327,0.00002111447,0.0001123997,0.0005180049,0.000003615983,0.0002761404,0.0006396525,0.0002647541,0.0004470464,0.1938291,0.8038742,0.00001009139],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002305538,0.232652,0.01749867,0.013235,0.003426721,0.00008540062,0.00008953767,0.00009808932,0.7306091],"genre_scores_gemma":[0.04825,0.2029473,0.0158879,0.01103965,0.003308201,0.000250902,0.0001349105,0.0002209365,0.7179602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01572694,"threshold_uncertainty_score":0.05261189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06913295008098583,"score_gpt":0.3292108700652892,"score_spread":0.2600779199843034,"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."}}