{"id":"W4255917440","doi":"10.1149/2.009201if","title":"Special Meeting Section · Montréal, Canada · May 10-14, 2020","year":2020,"lang":"en","type":"article","venue":"The Electrochemical Society Interface","topic":"Medical Research and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Government (linguistics); Section (typography); Library science; Engineering ethics; State (computer science); Panel discussion; Political science; Special section; Engineering; Public relations; Computer science; Engineering physics; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000120979,0.0001580879,0.0002281936,0.000003043021,0.0001326592,0.00001961055,0.0001734085,0.00009701641,0.001515877],"category_scores_gemma":[0.0007214975,0.0001019963,0.0001591716,0.000227424,0.00008413269,0.00002613537,0.00008773914,0.0006858219,0.00008789841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004526361,"about_ca_system_score_gemma":0.0003713918,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01936541,"about_ca_topic_score_gemma":0.005492983,"domain_scores_codex":[0.9983587,0.00004064306,0.0001956405,0.0002708958,0.0006439078,0.0004902487],"domain_scores_gemma":[0.9990768,0.0001584152,0.00004632326,0.0001524772,0.00008428785,0.0004817331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004059653,0.00004549759,0.0001037002,0.00003506635,0.0002164442,0.00001466253,0.00032791,0.000001088945,0.1637384,0.000004311765,0.8341769,0.0009300986],"study_design_scores_gemma":[0.001694575,0.000586545,0.00009889136,0.00006308021,0.0001186031,0.00004383842,0.0005327601,0.001935574,0.57773,0.0000411157,0.4169783,0.0001766941],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.67571,0.004624706,0.00462149,0.2708935,0.0006359292,0.001347448,0.00002256215,0.0002702234,0.04187417],"genre_scores_gemma":[0.983166,0.0003115754,0.0002972946,0.005310648,0.006056084,0.00002105781,0.00003091814,0.00002554824,0.004780819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4171986,"threshold_uncertainty_score":0.9993969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223217218249412,"score_gpt":0.2646685132982252,"score_spread":0.252436341115731,"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."}}