{"id":"W4401429991","doi":"10.36040/jati.v8i4.10337","title":"PREDIKSI ADOPSI HEWAN PELIHARAAN MENGGUNAKAN METODE XGBOOST","year":2024,"lang":"id","type":"article","venue":"JATI (Jurnal Mahasiswa Teknik Informatika)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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":[0.001101349,0.0009376911,0.001298534,0.0009656813,0.0007575852,0.003776931,0.0007968332,0.001248099,0.0237063],"category_scores_gemma":[0.00138745,0.0005334749,0.001145966,0.0009943671,0.0006074839,0.002102939,0.001300038,0.001706363,0.007074834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007072183,"about_ca_system_score_gemma":0.001284657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002046095,"about_ca_topic_score_gemma":0.00292877,"domain_scores_codex":[0.9991703,0.0001450928,0.00006403848,0.0001930576,0.0003022662,0.0001251369],"domain_scores_gemma":[0.9994457,0.000185514,0.00007000622,0.00007183511,0.0001699401,0.00005699732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003160344,0.000666964,0.01276331,0.004525097,0.0003756959,0.001007819,0.0009816812,0.003463231,0.3611464,0.009925537,0.0127886,0.5891953],"study_design_scores_gemma":[0.0004166801,0.002806439,0.02104127,0.001276353,0.001043784,0.003215065,0.002058115,0.01255954,0.3411561,0.01010298,0.6040624,0.0002611985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4396444,0.07529898,0.276996,0.007089564,0.00332721,0.001972556,0.005097751,0.006799177,0.1837744],"genre_scores_gemma":[0.656639,0.03672154,0.1524002,0.003775207,0.000673981,0.001439211,0.003988867,0.002300925,0.1420612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0237063,"threshold_uncertainty_score":0.07930541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417810789291214,"score_gpt":0.2719171737827221,"score_spread":0.2577390658898099,"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."}}