{"id":"W2154581575","doi":"10.1021/la8037704","title":"Pinning, Retraction, and Terracing of Evaporating Droplets Containing Nanoparticles","year":2009,"lang":"en","type":"article","venue":"Langmuir","topic":"Nanomaterials and Printing Technologies","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council","keywords":"Nanoparticle; Chemical engineering; Materials science; Nanotechnology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001509944,0.0003974864,0.0004378223,0.0003259426,0.0004899626,0.0006716109,0.0006454354,0.00087529,0.001403085],"category_scores_gemma":[0.0007934043,0.0002748613,0.0005177645,0.0002462087,0.0009398297,0.0009181044,0.0007592757,0.0005326576,0.000162682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005676756,"about_ca_system_score_gemma":0.0003992156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004221417,"about_ca_topic_score_gemma":0.002060842,"domain_scores_codex":[0.9999092,0.000009696233,0.000005219842,0.00001953485,0.00002715209,0.00002917025],"domain_scores_gemma":[0.9997941,0.00008601911,0.00004394525,0.00001779615,0.00001682878,0.00004133821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00030628,0.0001768622,0.005704035,0.0001861536,0.00007457587,0.004706722,0.0007266629,0.7784304,0.1738829,0.02442051,0.000315251,0.01106963],"study_design_scores_gemma":[0.00004486842,0.0001302126,0.00149,0.000008901167,0.00001273472,0.0002602908,0.0001141795,0.9742196,0.02023351,0.003012944,0.0004424935,0.00003018022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762657,0.000259692,0.018481,0.0001480688,0.00002389705,0.00005251356,0.00006167909,0.00004927982,0.004658246],"genre_scores_gemma":[0.9917625,0.0002039989,0.004728112,0.00003594272,0.00001016023,0.00003035251,0.00006094404,0.00001697437,0.003151037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004221417,"threshold_uncertainty_score":0.008393705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008551342676779062,"score_gpt":0.211750341419775,"score_spread":0.2031989987429959,"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."}}