{"id":"W4405813383","doi":"10.60126/maras.v2i3.426","title":"Capaian Kinerja Laboratorium Sebagai Baseline Penyusunan Renstra Laboratorium dengan Metode Statistik Deskriptif","year":2024,"lang":"id","type":"article","venue":"MARAS Jurnal Penelitian Multidisiplin","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Gynecology; Medicine; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006797175,0.001286853,0.001132884,0.001599523,0.001392494,0.004750596,0.001569249,0.001069432,0.02048233],"category_scores_gemma":[0.007589723,0.0007371567,0.001238476,0.002129246,0.0008077926,0.00197399,0.001908545,0.002174636,0.005901088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003714924,"about_ca_system_score_gemma":0.008622877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01690042,"about_ca_topic_score_gemma":0.03234462,"domain_scores_codex":[0.9926,0.001887013,0.0004276457,0.00135745,0.00311403,0.0006139465],"domain_scores_gemma":[0.9927582,0.00229892,0.0008843906,0.0006645516,0.002990701,0.0004031801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003596331,0.00217715,0.2540403,0.004361663,0.0007515856,0.0008934019,0.008003322,0.009674836,0.1082545,0.01610636,0.03643835,0.5557021],"study_design_scores_gemma":[0.0002660752,0.004027408,0.4332947,0.001341455,0.0009147226,0.0006529473,0.01730402,0.01684885,0.1601424,0.01231991,0.3524244,0.0004630094],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5716565,0.005557062,0.1464645,0.006770235,0.001226837,0.004396063,0.03121906,0.005320659,0.227389],"genre_scores_gemma":[0.7393282,0.002746519,0.1305129,0.001702496,0.0001389001,0.00460741,0.01179464,0.0008798057,0.1082891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02048233,"threshold_uncertainty_score":0.06852025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355614701973899,"score_gpt":0.2523376376791169,"score_spread":0.2387814906593779,"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."}}