{"id":"W4235393913","doi":"10.5539/jmsr.v9n2p71","title":"Reviewer acknowledgements for Journal of Materials Science Research, Vol. 9, No. 2","year":2020,"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 physics; Engineering ethics; Library science; 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":[0.193923,0.0004366301,0.001545258,0.002450402,0.002268773,0.003937904,0.01019269,0.0001704251,0.006724022],"category_scores_gemma":[0.4044043,0.0003173706,0.0002075372,0.005107813,0.009525327,0.004549962,0.002397594,0.0009528269,0.002240552],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008321778,"about_ca_system_score_gemma":0.007093594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002545573,"about_ca_topic_score_gemma":0.000001326349,"domain_scores_codex":[0.9777412,0.00308864,0.003521643,0.001146233,0.01163278,0.002869528],"domain_scores_gemma":[0.7524359,0.001743993,0.003406372,0.00128562,0.239127,0.002001099],"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":[0.0006339575,0.0001544195,0.00004445472,0.0005061151,0.00000891639,0.00002909008,0.0007231395,0.0001102346,0.9238235,0.0002099139,0.07341724,0.0003390248],"study_design_scores_gemma":[0.001011908,0.002783458,0.0004173637,0.000778983,0.0000224041,0.00005339412,0.0003216085,0.00009693538,0.9578496,0.001182538,0.03517393,0.0003078637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560598,0.00043492,0.0007209813,0.001171402,0.04000609,0.001136546,0.00007779161,0.00002770121,0.0003647634],"genre_scores_gemma":[0.9277763,0.0008124487,0.04400949,0.0004293622,0.02597614,0.00006241764,0.000002440565,0.0001229244,0.0008084932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2274943,"threshold_uncertainty_score":0.9999278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459357093100945,"score_gpt":0.4654921054354308,"score_spread":0.3195563961253364,"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."}}