{"id":"W6997931710","doi":"","title":"硫黄含量の多いアモルファス遷移金属硫化物TiS_3およびMoS_3を用いた高容量全固体リチウム電池の開発","year":2016,"lang":"ja","type":"other","venue":"Osaka Prefecture University Repository (Osaka Prefecture University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Hokkaido University; University of Waterloo","keywords":"Process (computing); Identification (biology); Product (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001531139,0.000711632,0.000477689,0.002895731,0.005709907,0.008080973,0.001491612,0.002257277,0.4718314],"category_scores_gemma":[0.005013818,0.0007184716,0.0006702406,0.00267558,0.0022606,0.004481001,0.002777633,0.002697418,0.2524969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00453933,"about_ca_system_score_gemma":0.008650657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05091663,"about_ca_topic_score_gemma":0.09855758,"domain_scores_codex":[0.9988542,0.00007733428,0.00008530461,0.0001479362,0.0006281213,0.0002072407],"domain_scores_gemma":[0.9967254,0.00028695,0.0001208165,0.0004843775,0.001763438,0.0006190204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001706983,0.0001608333,0.002347605,0.0001755916,0.000008543057,0.000266281,0.001748689,0.0001318527,0.0007020113,0.0847219,0.8301173,0.07944881],"study_design_scores_gemma":[0.00002126433,0.00002036926,0.004701131,0.0000931719,0.000009005516,0.0001312952,0.0007789679,0.0000905516,0.001026622,0.007198593,0.9859083,0.00002061014],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001997482,0.0001832177,0.0005625252,0.00135568,0.0004175175,0.00006663939,0.003715534,0.0004555952,0.9912458],"genre_scores_gemma":[0.01387481,0.0002904925,0.001138741,0.0003400615,0.0001350614,0.0000697158,0.00238096,0.0005585437,0.9812115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4718314,"threshold_uncertainty_score":0.7533683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004589284141915765,"score_gpt":0.1731074009494652,"score_spread":0.1685181168075495,"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."}}