{"id":"W4402937715","doi":"10.1007/978-3-031-72837-2_1","title":"Introduction: A Definitional Preamble","year":2024,"lang":"en","type":"book-chapter","venue":"The International library of ethics, law and technology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Preamble; History; Computer science; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001831751,0.0001649857,0.0002407584,0.0002836712,0.0001093559,0.00003198546,0.0002041507,0.0008023257,0.001909344],"category_scores_gemma":[0.00004871751,0.0001320559,0.00008603434,0.00006184055,0.000952956,0.0001145288,0.0001539853,0.001449698,0.0001961573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003074493,"about_ca_system_score_gemma":0.0003692279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007155985,"about_ca_topic_score_gemma":0.00007396893,"domain_scores_codex":[0.998795,0.000008104945,0.0004394276,0.0003397254,0.0002988173,0.0001188736],"domain_scores_gemma":[0.9990191,0.0002589601,0.0001472921,0.0002825685,0.0002451993,0.00004689015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004666221,0.00002622884,0.0000584213,0.0001632811,0.0001530769,0.0000113958,0.00009167601,4.384649e-7,0.00005426649,0.9803503,0.01669708,0.002347165],"study_design_scores_gemma":[0.00001655434,0.00008804526,0.000003989404,0.0001646516,0.00004445537,0.0001445293,0.00004935655,0.00001891137,0.0007450051,0.5002895,0.4983779,0.00005707778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002316978,0.002701285,0.00002393249,0.3444153,0.001862344,0.0002237374,0.00009361458,0.0001661709,0.650282],"genre_scores_gemma":[0.1500177,0.00339797,0.0008914266,0.00639407,0.005195483,0.00004488181,0.0007511843,0.000087252,0.83322],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4816808,"threshold_uncertainty_score":0.9990031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09297906403299971,"score_gpt":0.3450150943482791,"score_spread":0.2520360303152794,"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."}}