{"id":"W2915242072","doi":"10.1149/ma2018-02/15/2186","title":"(Invited) Using Image Recognition to Identify Platinum Surfaces with Cyclic Voltammetry Scans","year":2018,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Platinum; Durability; Commercialization; Cyclic voltammetry; Electrolyte; Materials science; Fuel cells; Electrode; Catalysis; Computer science; Chemical engineering; Electrochemistry; Business; Composite material; Chemistry; Engineering; Organic 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":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000736894,0.0006636154,0.0003179128,0.001240551,0.0004329921,0.001383802,0.000744441,0.001514393,0.02104866],"category_scores_gemma":[0.001354375,0.0003054567,0.0003472624,0.000529768,0.0005072141,0.0009879002,0.000623001,0.001335218,0.01823739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003134494,"about_ca_system_score_gemma":0.0002764192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235234,"about_ca_topic_score_gemma":0.005643875,"domain_scores_codex":[0.9995613,0.00003052818,0.00001692189,0.0001167058,0.000235828,0.00003874804],"domain_scores_gemma":[0.999124,0.0001172411,0.0000331496,0.00003967651,0.0005901156,0.00009582235],"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":[0.0004346645,0.0001150566,0.001679491,0.0005132919,0.00007416926,0.0004355303,0.0002504972,0.0004495506,0.1204946,0.001558458,0.3435717,0.530423],"study_design_scores_gemma":[0.00006520389,0.0003184465,0.006359475,0.0001378835,0.0001205211,0.001502687,0.00043459,0.004954612,0.1098432,0.001854178,0.8743021,0.0001070447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0794502,0.08654429,0.3577999,0.05973242,0.1652459,0.001071671,0.003185442,0.01072252,0.2362477],"genre_scores_gemma":[0.1416476,0.02642807,0.1332353,0.01507638,0.03118892,0.0004728121,0.002022426,0.001310714,0.6486177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02104866,"threshold_uncertainty_score":0.07041478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346644344909609,"score_gpt":0.2778287006784321,"score_spread":0.244362257229336,"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."}}