{"id":"W4242712715","doi":"10.1149/ma2017-02/32/1410","title":"Quantitative Mapping of Ionomer in Catalyst Layers by Electron and X-ray Spectromicroscopy","year":2017,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Automotive Fuel Cell Cooperation (Canada); McMaster University","funders":"","keywords":"Ionomer; Scanning transmission electron microscopy; Transmission electron microscopy; Materials science; Scanning electron microscope; Analytical Chemistry (journal); Chemistry; Polymer; Nanotechnology; Composite material; Copolymer; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003677294,0.0006978323,0.0003436289,0.0009178262,0.0002695334,0.0008129575,0.0004517749,0.000639316,0.001745576],"category_scores_gemma":[0.0004858769,0.0003367336,0.0002350748,0.0005005369,0.0005062973,0.0006512999,0.0004150744,0.0005398772,0.0004586044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004659246,"about_ca_system_score_gemma":0.0001864439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007896834,"about_ca_topic_score_gemma":0.001076262,"domain_scores_codex":[0.9997364,0.00001848497,0.00001892867,0.00007528821,0.0001136274,0.00003728452],"domain_scores_gemma":[0.9996025,0.00009619647,0.00009528443,0.00005054368,0.0001261411,0.00002927119],"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":[0.00003402961,0.00000898272,0.0002146567,0.00003457928,0.000004101802,0.0000268119,0.00002156022,0.0001332331,0.9981183,0.00003102766,0.00001784076,0.001355002],"study_design_scores_gemma":[0.000002297445,0.00004759062,0.002838562,0.000005702012,0.000009931349,0.00006119242,0.00002450237,0.001345348,0.9950023,0.00001953419,0.000638949,0.000004047495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643025,0.002027031,0.02973598,0.00007391517,0.00003397646,0.00007435152,0.001107681,0.0005884694,0.002056034],"genre_scores_gemma":[0.9432579,0.002356627,0.04921758,0.00006758455,0.00001744229,0.0001138014,0.0006464059,0.0002848237,0.004037799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001745576,"threshold_uncertainty_score":0.005839527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550604866004586,"score_gpt":0.2887501435215646,"score_spread":0.2732440948615187,"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."}}