{"id":"W4246697488","doi":"10.5539/jmsr.v6n1p62","title":"Reviewer acknowledgements for Journal of Materials Science Research, Vol. 6, No. 1","year":2016,"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 ethics; 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":[0.2605573,0.0004356913,0.001427073,0.0038206,0.002251745,0.002955666,0.00983185,0.000181776,0.008405046],"category_scores_gemma":[0.3981976,0.0002555011,0.0002029011,0.003771558,0.01270282,0.005447592,0.002168404,0.0005839496,0.002592591],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383479,"about_ca_system_score_gemma":0.007094906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002581974,"about_ca_topic_score_gemma":0.000002525778,"domain_scores_codex":[0.9773886,0.003265072,0.003502753,0.001111624,0.01153729,0.003194681],"domain_scores_gemma":[0.7317164,0.002853713,0.003433929,0.001696508,0.2589034,0.001396091],"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":[0.0005407627,0.0001906752,0.00007039871,0.000269647,0.000008657385,0.0000209767,0.0002399106,0.000014371,0.9411195,0.0003453337,0.05613097,0.001048828],"study_design_scores_gemma":[0.001228186,0.002082637,0.0008183771,0.001738401,0.00001842035,0.00008371487,0.0001350554,0.000009468906,0.9584971,0.003281888,0.0318009,0.0003058272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9416305,0.0003163286,0.0009676483,0.0006455799,0.05499607,0.0009869476,0.00008688342,0.00002305227,0.0003470041],"genre_scores_gemma":[0.9416029,0.001203989,0.03373272,0.0001171567,0.01955956,0.00008217344,0.000001040224,0.0001185575,0.003581911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473661,"threshold_uncertainty_score":0.9999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1172257767129876,"score_gpt":0.4647327274103892,"score_spread":0.3475069506974016,"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."}}