{"id":"W2519311317","doi":"10.2533/chimia.2016.662","title":"Thematic Platform in vitro Diagnostics Technological Progress with a Powerful Network","year":2016,"lang":"en","type":"article","venue":"CHIMIA International Journal for Chemistry","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Thematic map; Population; Business; Biotechnology; Engineering; Data science; Nanotechnology; Medicine; Computer science; Biology; Geography; Environmental health; Cartography","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":[0.009435674,0.001260284,0.0008710021,0.00242846,0.0008232155,0.004674822,0.002162071,0.002420771,0.04883983],"category_scores_gemma":[0.004692897,0.0006888115,0.0009332862,0.001215048,0.001215723,0.006533462,0.006234715,0.003647158,0.03679624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001349616,"about_ca_system_score_gemma":0.002870202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005060999,"about_ca_topic_score_gemma":0.0003564991,"domain_scores_codex":[0.9953565,0.001039443,0.0002013212,0.000801713,0.002219345,0.000381654],"domain_scores_gemma":[0.9945416,0.001073525,0.0004002538,0.0009749244,0.001709824,0.001299911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006651513,0.0002843655,0.001766995,0.001381039,0.0001066537,0.0004307379,0.0005119868,0.001895509,0.1117559,0.1184398,0.2201029,0.542659],"study_design_scores_gemma":[0.00006144425,0.0003127067,0.0005141043,0.0001937445,0.00004209805,0.0004951432,0.0000856242,0.002402026,0.02241741,0.01019374,0.963239,0.00004293495],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.024786,0.04958986,0.4710881,0.0390243,0.01327082,0.001453983,0.006370815,0.02356128,0.3708548],"genre_scores_gemma":[0.1608757,0.06724443,0.4115971,0.01371169,0.01093883,0.002507634,0.01995393,0.004489186,0.3086815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04883983,"threshold_uncertainty_score":0.1633855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0161835316574426,"score_gpt":0.2910298786039191,"score_spread":0.2748463469464765,"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."}}