{"id":"W4391043739","doi":"10.5194/amt-17-335-2024","title":"Measuring diameters and velocities of artificial raindrops with a neuromorphic event camera","year":2024,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eidgenössische Anstalt für Wasserversorgung Abwasserreinigung und Gewässerschutz; University of Toronto; Eidgenössische Technische Hochschule Zürich","keywords":"Disdrometer; Brightness; Computer science; Event (particle physics); Volume (thermodynamics); Trap (plumbing); Pixel; Range (aeronautics); Measure (data warehouse); Optics; Physics; Environmental science; Remote sensing; Precipitation; Meteorology; Geology; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001097839,0.0001956162,0.000259314,0.00003316312,0.0001031985,0.0001114104,0.0001356392,0.00004604834,0.0002760905],"category_scores_gemma":[0.00005948314,0.000147907,0.00007788706,0.0004549024,0.0001408451,0.0002311064,0.000009903343,0.0001289102,0.00000901486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001902671,"about_ca_system_score_gemma":0.00009701888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000662553,"about_ca_topic_score_gemma":0.0006055575,"domain_scores_codex":[0.9980453,0.0001226702,0.0003403647,0.0003273268,0.0009327807,0.0002315916],"domain_scores_gemma":[0.9994366,0.00006134529,0.00009476861,0.0001657719,0.0001539661,0.00008750533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000120107,0.00006149952,0.1383438,0.0005087362,0.000549562,0.00005976279,0.001512458,0.0005628166,0.01869827,0.0002465938,0.0006259601,0.8387104],"study_design_scores_gemma":[0.001420355,0.005973261,0.5571293,0.006173978,0.002554189,0.0001926597,0.005341638,0.1508199,0.2376446,0.01037727,0.01824256,0.004130284],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434875,0.006782014,0.04514077,0.0005379104,0.0002219102,0.000631432,0.00002082043,0.0005918975,0.002585755],"genre_scores_gemma":[0.9871287,0.0001778477,0.0125081,0.00006749433,0.00005184318,0.00001168567,0.000008385146,0.000009808996,0.00003607921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8345801,"threshold_uncertainty_score":0.6031471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05002594071118242,"score_gpt":0.2135121098441899,"score_spread":0.1634861691330075,"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."}}