{"id":"W4248444257","doi":"10.1088/1742-6596/1220/1/011001","title":"12th International Conference on Excitonic and Photonic Processes in Condensed Matter and Nano Materials (EXCON 2018)","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Photonics; Exciton; Engineering physics; Materials science; Nanotechnology; Condensed matter physics; Physics; Optoelectronics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007245428,0.0002446451,0.0005022503,0.000121925,0.0000760146,0.0006226912,0.0004699984,0.00007746414,0.004594405],"category_scores_gemma":[0.0001171183,0.0001981626,0.00002507803,0.0001148515,0.0003318522,0.001213406,0.00016741,0.0001961244,0.0001433606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004428807,"about_ca_system_score_gemma":0.0002677124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004437017,"about_ca_topic_score_gemma":0.00002444499,"domain_scores_codex":[0.9981825,0.0001417579,0.0005805367,0.0003476689,0.0004545143,0.0002930371],"domain_scores_gemma":[0.9985746,0.0001071881,0.000667418,0.0002058171,0.0003574477,0.0000875357],"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":[0.0002643001,0.00004472222,0.005104842,0.0001670285,0.000008298091,0.0000103768,0.0008284904,0.00004193879,0.9881378,0.005115012,0.00004504404,0.0002321479],"study_design_scores_gemma":[0.0008000116,0.0004366777,0.009482621,0.0005223825,0.00001100154,0.0000976771,0.0004711565,0.00008502912,0.9797421,0.00785446,0.000220246,0.0002766595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962239,0.00004168561,0.0001192712,0.0007647444,0.001115265,0.0001772535,0.00002833947,0.00001468999,0.001514838],"genre_scores_gemma":[0.9982376,0.0002108359,0.0008291993,0.000173601,0.0001214516,0.000006717365,0.00000308827,0.00001681567,0.0004007513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008395721,"threshold_uncertainty_score":0.9963155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01772170166701278,"score_gpt":0.2630954249718345,"score_spread":0.2453737233048217,"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."}}