{"id":"W4388113066","doi":"10.1051/matecconf/202338600001","title":"Preface","year":2023,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Philosophy","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":["insufficient_payload"],"category_scores_codex":[0.001001973,0.0001212115,0.000232248,0.0001326796,0.00009107731,0.0001148161,0.0006494377,0.00005164533,0.00590088],"category_scores_gemma":[0.0002144139,0.00009627858,0.00003352766,0.0003759463,0.0002670138,0.0001548576,0.0001839396,0.00005837648,0.002406344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007196056,"about_ca_system_score_gemma":0.0003246125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001789061,"about_ca_topic_score_gemma":0.00001776708,"domain_scores_codex":[0.9985915,0.0001099858,0.0002979832,0.0002798997,0.0004100435,0.0003106286],"domain_scores_gemma":[0.9992671,0.0001222476,0.0001711562,0.0002944203,0.00008659714,0.00005850091],"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":[0.00001102867,0.00001171019,0.006908963,0.00005959831,0.000001907335,0.00000345609,0.0001424837,0.0006109163,0.9771329,0.01330144,0.001366116,0.0004494432],"study_design_scores_gemma":[0.0002965458,0.0001626459,0.05351518,0.0001077177,0.00001101798,0.000007088086,0.0003406856,0.01151537,0.8971678,0.006564534,0.02996826,0.0003431404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624299,0.00001946629,0.0001465932,0.0003836025,0.0007012321,0.0001055681,0.00002153107,0.0004223083,0.03576974],"genre_scores_gemma":[0.9972559,0.00001928067,0.001131615,0.00003005512,0.00004251872,0.00001546712,0.000004744616,0.000008821106,0.001491619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07996511,"threshold_uncertainty_score":0.9983704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02420336861277075,"score_gpt":0.2827521838562957,"score_spread":0.2585488152435249,"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."}}