{"id":"W4404449633","doi":"10.1007/s00062-024-01470-8","title":"Mismatch Vs No Mismatch in Large Core—A Matter of Definition","year":2024,"lang":"en","type":"article","venue":"Clinical Neuroradiology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Core (optical fiber); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008780288,0.0006283044,0.00165015,0.002575458,0.001340248,0.004098578,0.002595353,0.002279089,0.002655705],"category_scores_gemma":[0.04362882,0.0003938503,0.000843832,0.002358024,0.007877097,0.006095615,0.003350626,0.003308585,0.0006299951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397503,"about_ca_system_score_gemma":0.00169724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001278696,"about_ca_topic_score_gemma":0.001335021,"domain_scores_codex":[0.9945313,0.001926513,0.001356323,0.0008641753,0.0008907275,0.0004309327],"domain_scores_gemma":[0.9813921,0.01052056,0.003850299,0.001035944,0.002014023,0.001187045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004042598,0.0003955161,0.4622309,0.003156345,0.001232836,0.005973547,0.004537121,0.00246248,0.005756531,0.180558,0.02045464,0.3091997],"study_design_scores_gemma":[0.0003949737,0.002051831,0.2510165,0.01100053,0.001917816,0.06336307,0.01591666,0.01179956,0.007201178,0.5232141,0.1116314,0.0004924123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6105614,0.1912727,0.07877441,0.06512497,0.01010519,0.0003990239,0.001200749,0.0002192258,0.04234226],"genre_scores_gemma":[0.9609233,0.01038907,0.01497455,0.006000771,0.006362384,0.0001619562,0.0003204265,0.0001309906,0.000736676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008780288,"threshold_uncertainty_score":0.04643512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06381577279089476,"score_gpt":0.3631207819993188,"score_spread":0.2993050092084241,"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."}}