{"id":"W2728152345","doi":"10.1149/ma2017-02/9/684","title":"Quantitative Examination of Porous Rust on Steel","year":2017,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Metallurgy and Material Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"AkzoNobel (Canada)","funders":"","keywords":"Porosity; Porosimetry; Microporous material; Materials science; Gas pycnometer; Mesoporous material; Macropore; Characterization (materials science); Corrosion; Rust (programming language); Composite material; Porous medium; 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":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001972915,0.00009596363,0.000154279,0.00003787627,0.0003799982,0.0001495273,0.0004216328,0.00005443941,0.0001638017],"category_scores_gemma":[0.001761014,0.00008139239,0.00002435103,0.00002817112,0.0001828804,0.0003140306,0.00007166927,0.000056266,0.0003308542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001764129,"about_ca_system_score_gemma":0.00003033912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003574261,"about_ca_topic_score_gemma":0.0000281698,"domain_scores_codex":[0.9988996,0.00007474118,0.0002691321,0.0002370232,0.0003170895,0.0002023991],"domain_scores_gemma":[0.9987281,0.0001559515,0.0005941213,0.0003764937,0.00008549444,0.00005981754],"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":[0.00002762171,0.00004622512,0.00003236809,0.00002042001,0.000001625192,0.000008140161,0.0003623437,0.00104214,0.9968523,0.00112616,0.00003440154,0.0004462573],"study_design_scores_gemma":[0.0001331096,0.0001276003,0.1248434,0.0001014981,0.000005125161,0.000001859801,0.0001339657,0.00003988239,0.8740515,0.0001908135,0.0002782999,0.00009302267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838817,0.000006989734,0.000003699744,0.00007155834,0.0007563329,0.0000708318,0.000005853529,0.00002495923,0.2151781],"genre_scores_gemma":[0.9967383,0.000002755083,0.0025955,0.00002095809,0.00006316436,0.000003706624,0.000001603648,0.000006890488,0.0005671007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.214611,"threshold_uncertainty_score":0.4252572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018229838802108,"score_gpt":0.3026591832736573,"score_spread":0.2524768848856362,"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."}}