{"id":"W2802940836","doi":"10.1016/j.cis.2018.04.008","title":"Microrheology, advances in methods and insights","year":2018,"lang":"en","type":"review","venue":"Advances in Colloid and Interface Science","topic":"Blood properties and coagulation","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Microrheology; Computer science; Soft matter; Tracking (education); Nanotechnology; Artificial intelligence; Physics; Materials science; Engineering; Rheology; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007552275,0.0002650243,0.0009921339,0.000411049,0.0001004399,0.00004362458,0.000228119,0.0001439611,0.00001295757],"category_scores_gemma":[0.0002374461,0.0001801456,0.00004466238,0.0009448173,0.001567578,0.0009009147,0.000241397,0.0003170387,0.000003658757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009077424,"about_ca_system_score_gemma":0.0002176331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001188788,"about_ca_topic_score_gemma":0.0001391216,"domain_scores_codex":[0.9982059,0.000126346,0.000489362,0.000713131,0.0001531573,0.0003121255],"domain_scores_gemma":[0.9992543,0.0001600464,0.0001767714,0.000243368,0.00007058125,0.00009494866],"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":[0.00002441471,0.00002732935,0.0001358316,0.002543741,0.000004046553,0.000001865618,0.0003123534,0.000001441291,0.0001816434,0.0002200736,0.00000639166,0.9965408],"study_design_scores_gemma":[0.0003087084,0.0003664144,0.00004301254,0.01005821,0.00005209181,0.00009355515,0.0001919392,0.0001295229,0.0005487593,0.0007339865,0.9872695,0.0002042829],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001376936,0.995052,0.0002072776,0.0000367184,0.0004490932,0.0005610807,0.000002250524,0.00001214387,0.002302459],"genre_scores_gemma":[0.004482273,0.9861938,0.008782121,0.0000718766,0.00004333312,0.00004646372,0.000001578512,0.00001279685,0.000365725],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9963366,"threshold_uncertainty_score":0.7346125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03196373994932954,"score_gpt":0.4429227675988069,"score_spread":0.4109590276494773,"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."}}