{"id":"W2773982084","doi":"10.1080/19475411.2017.1409822","title":"Flow velocity and temperature sensing using thermosensitive fluorescent polymer seed particles in water","year":2017,"lang":"en","type":"article","venue":"International Journal of Smart and Nano Materials","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; Office of Naval Research; National Science Foundation","keywords":"Microscale chemistry; Flow velocity; Particle image velocimetry; Materials science; Polymer; Flow (mathematics); Velocimetry; Temperature measurement; Particle (ecology); Fluorescence; Fluid dynamics; Mechanics; Optics; Thermodynamics; Composite material; Physics; Turbulence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001834164,0.0002162754,0.0001854192,0.0001689144,0.0001724725,0.0002779369,0.0002261267,0.0002404365,0.0002289234],"category_scores_gemma":[0.0002401295,0.0002077388,0.000125877,0.0001252232,0.000414803,0.0005012527,0.0002851474,0.0003073358,0.00008786656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003602317,"about_ca_system_score_gemma":0.0002385438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001365403,"about_ca_topic_score_gemma":0.001399404,"domain_scores_codex":[0.9999051,0.00001231866,0.000004844804,0.00003251933,0.00002777568,0.00001739026],"domain_scores_gemma":[0.9998804,0.00003913728,0.00004598753,0.000006539273,0.00001504213,0.00001282618],"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":[0.00001781119,0.000005467155,0.0001034913,0.00001149485,5.26638e-7,0.00001202428,0.00001794754,0.0002156298,0.9987196,0.0001107146,0.000007473855,0.0007778747],"study_design_scores_gemma":[0.000006757944,0.00006841672,0.0005500144,0.000003023568,0.000002665967,0.00001643433,0.00001262105,0.006696155,0.9923458,0.00004618406,0.0002466656,0.000005313525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796751,0.0004623847,0.01900915,0.00005161938,0.00001243803,0.00002102532,0.00003679826,0.00006856421,0.0006628847],"genre_scores_gemma":[0.9860978,0.0002930188,0.01291256,0.00002187934,0.000005651264,0.00002146235,0.00003297021,0.00001254364,0.000602052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001365403,"threshold_uncertainty_score":0.002714932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140574623765397,"score_gpt":0.2678443038189848,"score_spread":0.2464385575813308,"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."}}