{"id":"W2621376538","doi":"10.2514/6.2017-4350","title":"Comparison of Image Preprocessing Methods for Fuel Droplet Characterization","year":2017,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Preprocessor; Characterization (materials science); Computer science; Artificial intelligence; Image (mathematics); Computer vision; Image processing; Pattern recognition (psychology); Materials science; Nanotechnology","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":[0.001159191,0.0009209329,0.0007422578,0.002665803,0.0003684721,0.001539581,0.0009669017,0.000704217,0.004823881],"category_scores_gemma":[0.003772618,0.0004029734,0.00082088,0.00171487,0.0002177079,0.001016567,0.0005095112,0.0006781485,0.001170133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003633314,"about_ca_system_score_gemma":0.0007670106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003039187,"about_ca_topic_score_gemma":0.004231859,"domain_scores_codex":[0.9993364,0.0001074637,0.00006459569,0.0001028176,0.0003058117,0.00008288693],"domain_scores_gemma":[0.9972224,0.001111574,0.0001484673,0.0002669402,0.001177388,0.00007324577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002718578,0.0004025125,0.004417811,0.001083289,0.0002672315,0.0001280352,0.0001998565,0.008372609,0.2043528,0.001262998,0.003824999,0.7729694],"study_design_scores_gemma":[0.0002223532,0.001208591,0.03713121,0.000156472,0.0005366743,0.0008727916,0.0004368136,0.3309242,0.6100048,0.00142178,0.01690408,0.0001802803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3033623,0.006854691,0.6706212,0.0005994723,0.0004521983,0.0005739379,0.001687587,0.007413664,0.008434905],"genre_scores_gemma":[0.3937919,0.004328637,0.5911319,0.0002344589,0.0001233588,0.0002651612,0.003028798,0.001688766,0.005407073],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004823881,"threshold_uncertainty_score":0.01613754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07944844191350472,"score_gpt":0.4840681301502965,"score_spread":0.4046196882367918,"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."}}