{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002874905,0.00009656871,0.0001293772,0.00002518358,0.0001007823,0.0001222935,0.0001665721,0.0000569427,0.000005325614],"category_scores_gemma":[0.00007651869,0.00004006798,0.00008786877,0.00007462631,0.00002085412,0.0004250825,0.00001819071,0.00007935805,6.411879e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005649711,"about_ca_system_score_gemma":0.00002886143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001187683,"about_ca_topic_score_gemma":0.00003460759,"domain_scores_codex":[0.9989338,0.00004298805,0.0003345933,0.0001570128,0.000419315,0.0001122857],"domain_scores_gemma":[0.9987261,0.0001430331,0.000312293,0.00002820603,0.0007019744,0.0000883957],"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":[0.0004078114,0.001148085,0.00252634,0.00002009628,0.0001923627,0.000031078,0.0002196743,0.6126254,0.2613529,0.006303985,0.0008265865,0.1143457],"study_design_scores_gemma":[0.0004746534,0.0006274796,0.0377474,0.00007507654,0.0000375213,0.00007571598,0.0001286056,0.9481511,0.01104353,0.001353378,0.0001891243,0.00009646463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958163,0.0002322929,0.03356197,0.005842065,0.001618726,0.0002281767,0.0002030765,0.00004005732,0.0001105979],"genre_scores_gemma":[0.9950908,0.00005944577,0.003714365,0.0003922664,0.0005277669,0.000004584339,0.0001647087,9.388118e-7,0.0000450546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3355257,"threshold_uncertainty_score":0.1633925,"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."}}