{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123034,0.0008793314,0.0009845687,0.001047727,0.0004642376,0.0007739943,0.001015182,0.0009183506,0.002190744],"category_scores_gemma":[0.002021727,0.0004448212,0.0008403859,0.001092272,0.0006234717,0.0008133015,0.0007467641,0.0006348097,0.0003315674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966256,"about_ca_system_score_gemma":0.001238858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004993869,"about_ca_topic_score_gemma":0.007594339,"domain_scores_codex":[0.9994759,0.0002828224,0.00001544583,0.00008223737,0.00009572126,0.00004795491],"domain_scores_gemma":[0.9993348,0.000453399,0.00005172015,0.0000365215,0.0000999345,0.0000236519],"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":[0.00001482546,0.00003157232,0.0004409561,0.00004652956,0.00002900053,0.00003497795,0.0000265821,0.9575797,0.0005732545,0.005762895,0.0005197422,0.03493989],"study_design_scores_gemma":[0.000006933516,0.0000215148,0.00006665988,0.000006834999,0.000006768434,0.00001286278,0.00001295992,0.9952975,0.0001037712,0.003921753,0.0005388028,0.000003722119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01018757,0.0003314772,0.9869059,0.0001472489,0.00001659515,0.0000473803,0.0000277095,0.0001522468,0.002183803],"genre_scores_gemma":[0.2555068,0.0005313881,0.7408967,0.0001122402,0.00003636747,0.000250329,0.0001264407,0.0001182829,0.002421487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004993869,"threshold_uncertainty_score":0.009929597,"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."}}