{"id":"W3018522147","doi":"10.1039/d0lc00248h","title":"Open sessile droplet viscometer with low sample consumption","year":2020,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"CMC Microsystems (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Viscometer; Sample (material); Materials science; Sessile drop technique; Chemistry; Composite material; Contact angle; Chromatography; Viscosity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001448668,0.001327524,0.001294179,0.00221273,0.0006727505,0.00100633,0.001708529,0.001108383,0.005020304],"category_scores_gemma":[0.002239697,0.0006551563,0.0005278192,0.001528998,0.0005264761,0.001031915,0.001251613,0.002610537,0.003794375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00032672,"about_ca_system_score_gemma":0.00103657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000399167,"about_ca_topic_score_gemma":0.001047274,"domain_scores_codex":[0.9967378,0.0002204944,0.0002171942,0.0007234038,0.001958034,0.000143121],"domain_scores_gemma":[0.9978922,0.0006428261,0.0002680367,0.0003745564,0.0006751869,0.0001472276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001367645,0.0002624954,0.0008048376,0.0002068112,0.00002668146,0.0001262131,0.0001120703,0.0004693284,0.947945,0.002006978,0.001675986,0.04622685],"study_design_scores_gemma":[0.0000716892,0.0005194873,0.003653868,0.00004961931,0.000112363,0.0007600093,0.0000918588,0.02641471,0.9313247,0.001515343,0.03538302,0.000103276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1092753,0.00305261,0.8577021,0.0002883362,0.0008507412,0.001714408,0.003809941,0.008571366,0.0147352],"genre_scores_gemma":[0.1972512,0.003264672,0.7588191,0.0003813205,0.0002899431,0.004725059,0.004107566,0.001384355,0.02977681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005020304,"threshold_uncertainty_score":0.01679456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415066569162645,"score_gpt":0.2339911268790037,"score_spread":0.2098404611873773,"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."}}