{"id":"W4400486010","doi":"10.61091/jcmcc120-06","title":"Optimizing Rural Landscape Planning and Design: A Random Forest Algorithm Approach for Sustainable Development","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sustainable development; Random forest; Landscape planning; Environmental planning; Computer science; Environmental resource management; Geography; Agroforestry; Environmental science; Artificial intelligence; Political science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001554388,0.0002967843,0.0007138107,0.0003160174,0.0002695663,0.0006109892,0.000180961,0.0001398403,0.000002170708],"category_scores_gemma":[0.0001139859,0.0002529229,0.000146155,0.0002753667,0.00003635318,0.0002674982,0.00008445972,0.0003262027,3.957674e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008322499,"about_ca_system_score_gemma":0.00008547777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002056765,"about_ca_topic_score_gemma":4.200959e-8,"domain_scores_codex":[0.998244,0.00004532472,0.0008161045,0.0001625817,0.0003354941,0.0003964549],"domain_scores_gemma":[0.9985741,0.0007249281,0.0001821345,0.00009496878,0.0002512775,0.0001725826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001141946,0.001171246,0.0003719436,0.01365733,0.006016144,0.0007642172,0.04563622,0.2230761,0.001440345,0.626257,0.008322581,0.07214498],"study_design_scores_gemma":[0.003501817,0.0002817552,0.000007029919,0.0003988738,0.00022156,0.00008993652,0.001658833,0.9299814,0.0003131123,0.06251565,0.0007025418,0.0003275244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04176394,0.005376571,0.949833,0.00001178249,0.00226739,0.0003924303,7.647459e-7,0.00009363556,0.0002604664],"genre_scores_gemma":[0.8172339,0.00004551452,0.1816814,0.000003767158,0.0009444655,0.00001090718,0.00000343418,0.00005285251,0.00002380233],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.77547,"threshold_uncertainty_score":0.9999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128460501930889,"score_gpt":0.2202691695114792,"score_spread":0.2074231193183903,"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."}}