{"id":"W2607317540","doi":"10.1149/ma2010-03/1/734","title":"Using Image Analysis to Extract Quantitative Data from Li-Ion Battery Micrographs","year":2010,"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":"Hydro-Québec","funders":"","keywords":"Micrograph; Electron micrographs; Ion; Artificial intelligence; Battery (electricity); Materials science; Computer vision; Computer science; Chemistry; Optics; Physics; Composite material; Scanning electron microscope; Electron microscope","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":[0.0009240165,0.000220058,0.000329553,0.000444741,0.0001504234,0.0002506085,0.0002995628,0.0002121884,0.00008417379],"category_scores_gemma":[0.0004214779,0.0002253103,0.0001218223,0.0008229326,0.00002067874,0.0003766271,0.00008068955,0.0005155492,0.0002181948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003257486,"about_ca_system_score_gemma":0.00001841098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328124,"about_ca_topic_score_gemma":0.0005434442,"domain_scores_codex":[0.9984141,0.00005243614,0.0005247755,0.0004320863,0.0002572121,0.0003194157],"domain_scores_gemma":[0.9985359,0.0003271395,0.0001442161,0.0007446462,0.00008408614,0.0001640342],"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":[0.00001436307,0.00001757834,0.0007945556,0.000008481249,0.0002387081,0.00001664495,0.000187427,0.08108257,0.916169,3.345853e-7,0.000864595,0.0006057474],"study_design_scores_gemma":[0.000602817,0.000067243,0.04560059,0.0002352736,0.0008388442,0.00001857617,0.0007716542,0.2253998,0.7111287,0.00004289595,0.0142523,0.001041348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886754,0.00006592935,0.00281689,0.000019864,0.002053825,0.0001720997,0.00016102,0.0002692475,0.005765686],"genre_scores_gemma":[0.9772334,0.000002862005,0.02199916,0.00001846347,0.0005771972,0.000003697156,0.0001071415,0.00004432459,0.00001376005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2050403,"threshold_uncertainty_score":0.9187889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06871697189631523,"score_gpt":0.3171986859374736,"score_spread":0.2484817140411583,"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."}}