{"id":"W2489666853","doi":"10.1016/j.jenvman.2016.06.034","title":"Modelling spatial association in pattern based land use simulation models","year":2016,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Categorical variable; Land use, land-use change and forestry; Land use; Watershed; Measure (data warehouse); Calibration; Spatial ecology; Variable (mathematics); Econometrics; Environmental resource management; Geography; Environmental science; Computer science; Statistics; Data mining; Ecology; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003859568,0.00009988999,0.000134224,0.00006703708,0.00003404471,0.00002908536,0.0001276965,0.00004546235,0.0009990971],"category_scores_gemma":[0.000002470141,0.00006670093,0.00006141427,0.00004393271,0.000005389952,0.00076483,0.00007707745,0.00005373661,0.0001710515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007210179,"about_ca_system_score_gemma":0.000001361395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001703365,"about_ca_topic_score_gemma":0.0001534849,"domain_scores_codex":[0.998805,0.00005755007,0.0003679145,0.0001430183,0.0004508706,0.0001756117],"domain_scores_gemma":[0.9994444,0.00007530837,0.0003108923,0.0001122485,0.000001770557,0.0000554123],"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":[0.00002498368,0.00008284592,0.3135152,0.000005164661,0.00001313378,0.000015375,0.00002864452,0.6794451,0.000079654,5.057444e-7,0.00002008473,0.006769303],"study_design_scores_gemma":[0.001337658,0.00005032615,0.1530849,0.0000820553,0.00003137392,0.000001007405,0.00001755521,0.8439041,0.00006895902,0.0002699473,0.001031179,0.0001209558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810025,0.000008058403,0.1182812,0.0001578022,0.00009457189,0.0001196639,0.00000745002,0.000003967774,0.0003247962],"genre_scores_gemma":[0.9991632,0.00008647762,0.000430696,0.0001134211,0.00004105619,0.00000276808,0.000001785787,0.00001023025,0.0001504341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1644589,"threshold_uncertainty_score":0.9999141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454262073856494,"score_gpt":0.1920800010406032,"score_spread":0.1775373803020382,"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."}}