{"id":"W4379535585","doi":"10.5539/jmsr.v12n1p65","title":"Reviewer acknowledgements for Journal of Materials Science Research, Vol. 12, No. 1","year":2023,"lang":"en","type":"article","venue":"Journal of Materials Science Research","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science; Engineering ethics; Library science; Engineering physics; Computer science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch","sts","insufficient_payload"],"category_scores_codex":[0.3017203,0.0004434259,0.001484597,0.005763139,0.002940355,0.004154504,0.01004801,0.0001907604,0.004882755],"category_scores_gemma":[0.3209482,0.0003226101,0.0002153572,0.007355115,0.01008728,0.004467701,0.002735452,0.0008693525,0.004590581],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056071,"about_ca_system_score_gemma":0.006956972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003980297,"about_ca_topic_score_gemma":0.000003866026,"domain_scores_codex":[0.9755076,0.003240488,0.003560868,0.001148199,0.01293738,0.00360553],"domain_scores_gemma":[0.8282002,0.002336571,0.002858069,0.001547236,0.1637877,0.001270277],"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":[0.0003933443,0.000147562,0.00005686338,0.0004306879,0.000008947972,0.00004207836,0.0004733084,0.0001453689,0.8991702,0.0002385168,0.09846818,0.0004249167],"study_design_scores_gemma":[0.0009513929,0.001845679,0.001294181,0.00113267,0.0000199166,0.0000824193,0.0003682581,0.00009667574,0.9593698,0.002928227,0.03158792,0.0003228606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431152,0.0002267532,0.0002106099,0.0004876687,0.05426419,0.001103692,0.00008343377,0.0000470433,0.000461397],"genre_scores_gemma":[0.9390889,0.001927487,0.02931824,0.0001646965,0.02304066,0.0001516384,0.000006601965,0.0001937713,0.006108032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1508503,"threshold_uncertainty_score":0.9999226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150849517875085,"score_gpt":0.4789561511910376,"score_spread":0.3281066333159526,"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."}}