{"id":"W4243382746","doi":"10.5539/jmsr.v9n1p48","title":"Reviewer acknowledgements for Journal of Materials Science Research, Vol. 9, No. 1","year":2019,"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; Library science; Engineering physics; Engineering ethics; 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":[0.2577779,0.0004405874,0.001582434,0.0039188,0.001830763,0.003879613,0.01000518,0.0001907042,0.01176295],"category_scores_gemma":[0.2542622,0.0003153724,0.0002116184,0.0044227,0.007855266,0.005074746,0.002242789,0.0009119015,0.004956622],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185221,"about_ca_system_score_gemma":0.007262584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003786875,"about_ca_topic_score_gemma":0.000001993229,"domain_scores_codex":[0.9772679,0.002988229,0.003423924,0.001139375,0.01209323,0.003087345],"domain_scores_gemma":[0.769671,0.001861431,0.003155122,0.001666961,0.2225298,0.001115727],"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":[0.0005380439,0.0002083041,0.0001492668,0.0005247432,0.000009260242,0.0000153872,0.0003702126,0.0001147918,0.9596814,0.0003175017,0.03777811,0.0002930133],"study_design_scores_gemma":[0.001139619,0.002500999,0.00107817,0.001258845,0.00001835516,0.00007805534,0.0002848309,0.00005722532,0.9632565,0.001870141,0.02813208,0.0003251633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.929481,0.000341494,0.0002012938,0.0002442993,0.06767329,0.00133688,0.00005431513,0.00001970273,0.0006477375],"genre_scores_gemma":[0.9538065,0.0006100592,0.0284359,0.000125765,0.01326772,0.00005591565,0.000002166376,0.00010857,0.003587377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2104366,"threshold_uncertainty_score":0.9999298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09952846409557424,"score_gpt":0.459127774801717,"score_spread":0.3595993107061428,"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."}}