{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002625632,0.00008417706,0.0001086747,0.00001896479,0.00003142286,0.00008420437,0.00009742176,0.00003747914,0.0005236801],"category_scores_gemma":[0.00001474581,0.00006470091,0.00002102979,0.0001116833,0.0000143971,0.00007327986,0.00002492884,0.0001197366,0.0002567882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001431428,"about_ca_system_score_gemma":0.000003271079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001570901,"about_ca_topic_score_gemma":0.00001605432,"domain_scores_codex":[0.9995914,0.00001161175,0.00007604828,0.000128421,0.00007795625,0.0001145579],"domain_scores_gemma":[0.9997715,0.00003179254,0.00001115833,0.00009490331,0.000007710183,0.00008293749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003223894,0.001218131,0.05859268,0.004378772,0.001665997,0.0002631416,0.004840986,0.0874119,0.3520993,0.03048681,0.1010997,0.3547187],"study_design_scores_gemma":[0.004926125,0.002227182,0.05951087,0.0004639069,0.0001292333,0.00001636514,0.0001149538,0.6489813,0.1363917,0.0008349813,0.1447804,0.001623013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851805,0.00001960415,0.007531846,0.0006762783,0.00008728474,0.0001851276,0.00004481776,0.0002605257,0.006014047],"genre_scores_gemma":[0.9985646,0.00001197716,0.0005987668,0.0006074316,0.00007256846,0.000006916457,0.00001087117,0.00001905488,0.0001077986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5615694,"threshold_uncertainty_score":0.573393,"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."}}