{"id":"W3155007818","doi":"10.2316/j.2021.206-0615","title":"AN IMPROVED BOOSTING-BIPLS MODELS BASED ON WEIGHT ADJUSTMENT FOR SOIL HEAVY METAL CONTENT PREDICTION","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Agriculture, Soil, Plant Science","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Boosting (machine learning); Content (measure theory); Heavy metals; Environmental science; Soil science; Computer science; Artificial intelligence; Mathematics; Environmental chemistry; Chemistry; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001128678,0.0009286406,0.001177827,0.0005557959,0.0005131272,0.0008867342,0.001948552,0.0008554792,0.002169352],"category_scores_gemma":[0.001755783,0.000607165,0.001061136,0.0007923382,0.0004119131,0.001028284,0.0009354945,0.001260597,0.0007902184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000497941,"about_ca_system_score_gemma":0.001000085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01087912,"about_ca_topic_score_gemma":0.006708409,"domain_scores_codex":[0.9995537,0.0001125107,0.00002454932,0.000117041,0.0001278012,0.00006428283],"domain_scores_gemma":[0.999416,0.0002045285,0.00005376671,0.00003046227,0.0002657801,0.000029552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009940252,0.00005652027,0.002066963,0.0001087913,0.0000840334,0.00005473788,0.00007567505,0.9106783,0.002933604,0.00409172,0.001715903,0.07803439],"study_design_scores_gemma":[0.000003092462,0.00001283381,0.0001452237,0.000002937506,0.000007935238,0.000004418373,0.000002615324,0.9985683,0.0002166714,0.0007265853,0.0003053815,0.000003942876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02049183,0.0005419905,0.9764106,0.0001505139,0.00007934467,0.00003782701,0.0001033656,0.000517525,0.001666942],"genre_scores_gemma":[0.7792345,0.001026332,0.2096521,0.0002654876,0.0001602454,0.0003073484,0.0007832487,0.0002048706,0.008365815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01087912,"threshold_uncertainty_score":0.0216316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03139711497446079,"score_gpt":0.2383939761546851,"score_spread":0.2069968611802243,"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."}}