{"id":"W2852870","doi":"10.1016/0378-1135(88)90089-2","title":"間接材購買アウトソーシング(BTO)--ユーザー企業の知恵とIBMサービスのベスト・ミックスで、支出削減などの効果を最大化","year":2007,"lang":"en","type":"article","venue":"Provision","topic":"Herpesvirus Infections and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; IBM; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001845175,0.0001813759,0.0001224879,0.000219436,0.0002076046,0.0002381626,0.00009507795,0.0001573791,0.00193221],"category_scores_gemma":[0.0001577393,0.00008964642,0.0001236275,0.0002123993,0.0002536488,0.0001472872,0.00009317535,0.0002931925,0.0006072859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003288513,"about_ca_system_score_gemma":0.0002914687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005059062,"about_ca_topic_score_gemma":0.008145239,"domain_scores_codex":[0.9998562,0.00002431941,0.00001017099,0.00002232538,0.00004959446,0.00003732806],"domain_scores_gemma":[0.9998723,0.00002519759,0.00002734807,0.000008806955,0.00003822379,0.00002807699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002192542,0.00006393758,0.00487747,0.00006131695,0.00001414441,0.00006591586,0.00008214793,0.00005475781,0.9866405,0.0002189659,0.00007211726,0.007629497],"study_design_scores_gemma":[0.00002493356,0.002002192,0.07667726,0.00003291007,0.00004471055,0.001092398,0.0002290992,0.0006060242,0.908439,0.0003095579,0.01053035,0.00001154046],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903513,0.001817647,0.002619053,0.00005525555,0.00002695129,0.00007295109,0.0002107655,0.00001632944,0.004829775],"genre_scores_gemma":[0.9871071,0.001209921,0.00471655,0.00005478935,0.00001216368,0.00003233814,0.0008701175,0.00001031397,0.005986841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005059062,"threshold_uncertainty_score":0.01005924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200854442703252,"score_gpt":0.3308986141135697,"score_spread":0.3188900696865372,"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."}}