{"id":"W4387310361","doi":"10.1063/5.0167659","title":"Understanding under-liquid drop spreading using dynamic contact angle modeling","year":2023,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Physics; Scaling; Drop (telecommunication); Scaling law; Mechanics; Viscosity; Contact angle; Inertial frame of reference; Viscous liquid; Thermodynamics; Classical mechanics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000360585,0.0001451642,0.0002480593,0.00008307707,0.0002021052,0.00005318485,0.0002122563,0.00005522198,0.00007182481],"category_scores_gemma":[0.00002494249,0.0001512469,0.00009088513,0.0004323234,0.00006285131,0.0003013101,0.00006586168,0.00007979212,0.0000969225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547651,"about_ca_system_score_gemma":0.00007192596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002046537,"about_ca_topic_score_gemma":0.000006563517,"domain_scores_codex":[0.9987524,0.00005801888,0.0002842991,0.0002871233,0.0003043675,0.0003137674],"domain_scores_gemma":[0.9994057,0.00008657143,0.00006326155,0.0003065361,0.00006858767,0.00006928858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002158554,0.00002498706,0.00006152147,0.0000197913,0.000007865275,0.000001246569,0.0004943585,0.117714,0.8725727,0.009059464,0.000008296382,0.00001410271],"study_design_scores_gemma":[0.0001717568,0.00003109834,0.00001630028,0.00004068388,0.00001350375,0.00000113078,0.0006727786,0.7175574,0.2750944,0.006265672,0.00000154635,0.0001337596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6539668,0.0000226782,0.3452668,0.00004598984,0.0002015195,0.00007609531,0.00001795908,0.0001267046,0.0002754561],"genre_scores_gemma":[0.9990538,0.00001852989,0.0007793778,0.00003328238,0.00004094511,0.000002808348,0.00001697126,0.00002983357,0.00002446763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5998433,"threshold_uncertainty_score":0.6167668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1875520245725724,"score_gpt":0.3212762083784087,"score_spread":0.1337241838058363,"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."}}