{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005406133,0.0006400741,0.0004956956,0.001525605,0.001604226,0.007180637,0.001480852,0.00187289,0.009172408],"category_scores_gemma":[0.01719956,0.0005537175,0.001101816,0.001262879,0.01323526,0.006440918,0.005366321,0.004123009,0.002053506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002182574,"about_ca_system_score_gemma":0.002239642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344627,"about_ca_topic_score_gemma":0.001504722,"domain_scores_codex":[0.9957236,0.002642803,0.0001812046,0.0005105092,0.0007338017,0.0002080136],"domain_scores_gemma":[0.9823673,0.01314251,0.0004185816,0.002764345,0.0008111535,0.0004960796],"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":[0.00001488129,0.00002915049,0.00080199,0.0002325842,0.00001839109,0.00009210852,0.003994934,0.001680756,0.0007150005,0.9477063,0.0016381,0.04307583],"study_design_scores_gemma":[0.00001258099,0.0000212732,0.0003605532,0.0001052227,0.00001418933,0.0002518417,0.0005795671,0.004424158,0.001091861,0.9187952,0.07432464,0.00001907045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01966655,0.003633599,0.7396442,0.01551145,0.0005162496,0.0001562281,0.0001121452,0.0009854137,0.2197741],"genre_scores_gemma":[0.6212871,0.003267881,0.3316895,0.002236301,0.0005468887,0.0002660779,0.0002300651,0.0005411997,0.03993512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009172408,"threshold_uncertainty_score":0.03068477,"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."}}