{"id":"W3216414345","doi":"10.1016/j.ultramic.2021.113435","title":"Thermal characterization of morphologically diverse copper phthalocyanine thin layers by scanning thermal microscopy","year":2021,"lang":"en","type":"article","venue":"Ultramicroscopy","topic":"Thermal properties of materials","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Silesian University of Technology; Institut National de la Recherche Agronomique; Providence Health Care","keywords":"Scanning thermal microscopy; Materials science; Thin film; Copper; Analytical Chemistry (journal); Surface roughness; Microscopy; Optical microscope; Surface finish; Scanning electron microscope; Nanotechnology; Optics; Composite material; Chemistry; Thermal conductivity; Metallurgy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000520675,0.000357968,0.0005442966,0.00004317483,0.000212214,0.0002132278,0.0005854338,0.0002233795,0.00806931],"category_scores_gemma":[0.0001116499,0.000304437,0.0001185296,0.0001367674,0.0004003452,0.0004789694,0.0002354785,0.0001722436,0.0005995193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007193007,"about_ca_system_score_gemma":0.0001170104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001145285,"about_ca_topic_score_gemma":0.000001214998,"domain_scores_codex":[0.9974369,0.0003342047,0.0006512282,0.0006130302,0.0003647076,0.000599887],"domain_scores_gemma":[0.9986584,0.00006359045,0.0003875899,0.0005321742,0.0002334921,0.0001247151],"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":[0.0004004452,0.0002071855,0.0008492043,0.00005952431,0.00002353363,0.00003662581,0.0005908172,0.0001517146,0.997013,0.00001682323,0.0004886076,0.0001624589],"study_design_scores_gemma":[0.000785658,0.0001856884,0.004771583,0.00008988244,0.00003037915,0.00002175629,0.0001793566,0.00001652986,0.9928398,0.000003070448,0.0007290205,0.0003472758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968043,0.0001879862,0.0001374046,0.0001954927,0.0008895981,0.0002875289,0.0006793879,0.0001639523,0.0006543262],"genre_scores_gemma":[0.9949359,0.00003719839,0.002893929,0.0008318297,0.0001171664,0.00001706214,0.0002601321,0.00006297071,0.0008438312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007469791,"threshold_uncertainty_score":0.9999408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394501287903134,"score_gpt":0.2462074105573014,"score_spread":0.2322623976782701,"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."}}