{"id":"W7116902610","doi":"10.30557/qw000099","title":"Generative AI’s particular contributions to Knowledge Building","year":2025,"lang":"","type":"article","venue":"Qwerty","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Relevance (law); Rhetoric; Harmony (color); Body of knowledge; Knowledge base; Knowledge building; Knowledge-based systems","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":[],"category_scores_codex":[0.0007785875,0.0002925018,0.0004978406,0.0003210063,0.0007491264,0.000101358,0.0001889612,0.0003050969,0.000720182],"category_scores_gemma":[0.00315536,0.0002722988,0.0001773671,0.001373785,0.0001437938,0.0001356365,0.000110937,0.0005378379,0.001682469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095206,"about_ca_system_score_gemma":0.002410407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009371908,"about_ca_topic_score_gemma":0.0003363813,"domain_scores_codex":[0.9971935,0.0002552534,0.0009154459,0.0006174122,0.0002063393,0.0008120053],"domain_scores_gemma":[0.9965627,0.0004331819,0.00009140212,0.00061222,0.001729175,0.0005713585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004528561,0.002113289,0.01537911,0.0005984551,0.0005765304,0.00003117467,0.02046751,0.000444617,0.03808086,0.1647657,0.448154,0.3089359],"study_design_scores_gemma":[0.0001897622,0.0005208285,0.002759013,0.001483684,0.0005341267,0.000006241374,0.003728167,0.01125624,0.4738021,0.01495723,0.49031,0.0004525816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3072424,0.01914528,0.3340432,0.3031392,0.01819452,0.003807083,0.0001419644,0.0002016397,0.01408469],"genre_scores_gemma":[0.9771645,0.0002797108,0.0006389845,0.01127651,0.001762922,0.0002434657,0.00003229915,0.00001957487,0.00858202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6699222,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051036041985387,"score_gpt":0.5070439025463198,"score_spread":0.4019402983477811,"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."}}