{"id":"W2755202310","doi":"10.1038/sdata.2017.127","title":"Machine-learned and codified synthesis parameters of oxide materials","year":2017,"lang":"en","type":"article","venue":"Scientific Data","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Scale (ratio); Artificial intelligence; Computation; Machine learning; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007046425,0.0002122588,0.0004325382,0.000173397,0.001171226,0.002743698,0.005080384,0.0000730695,0.001316463],"category_scores_gemma":[0.005893897,0.0001760664,0.0000264463,0.0001306807,0.002276454,0.001188346,0.003334892,0.00008187009,0.0003261143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001454268,"about_ca_system_score_gemma":0.0000900756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008478404,"about_ca_topic_score_gemma":0.00004708383,"domain_scores_codex":[0.9968393,0.0002883668,0.0005175853,0.00126154,0.0006340147,0.000459137],"domain_scores_gemma":[0.9933129,0.000259565,0.00073392,0.005453164,0.00008859202,0.0001518389],"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":[0.00003957024,0.00002881023,0.0007509161,0.00007976012,0.000005062488,0.000005742706,0.00007747504,0.00001537471,0.9948184,0.0007272279,0.001825458,0.001626166],"study_design_scores_gemma":[0.0002514534,0.00002497015,0.01218139,0.0001119491,0.00004337912,0.00001803394,0.00005225513,0.002255737,0.9796073,0.001075042,0.004071803,0.0003066681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905173,0.00005663545,0.000326412,0.0005414702,0.002515672,0.0002290415,0.004778587,0.00007816018,0.0009566776],"genre_scores_gemma":[0.9699031,0.0000148819,0.02877776,0.00002903081,0.00003262085,0.00001170636,0.0001499411,0.00002008623,0.001060864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02845135,"threshold_uncertainty_score":0.9995965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08278359054886814,"score_gpt":0.3268148658449304,"score_spread":0.2440312752960623,"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."}}