{"id":"W4402828015","doi":"10.1016/j.ecoinf.2024.102832","title":"Simulating multi-scale optimization and variable selection in species distribution modeling","year":2024,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scale (ratio); Computer science; Selection (genetic algorithm); Variable (mathematics); Distribution (mathematics); Artificial intelligence; Mathematics; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004724352,0.0006067621,0.0008854994,0.0007267337,0.0004911588,0.0007009786,0.001330395,0.0009846508,0.001000295],"category_scores_gemma":[0.01219236,0.0004484729,0.0009725328,0.0008367012,0.001139061,0.001085791,0.001087229,0.001151626,0.0001038055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127045,"about_ca_system_score_gemma":0.0008347387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009643817,"about_ca_topic_score_gemma":0.007071505,"domain_scores_codex":[0.9987305,0.0009031441,0.00004410412,0.000142513,0.00009398573,0.00008591687],"domain_scores_gemma":[0.9907703,0.007783166,0.0005315837,0.0004798466,0.0002685063,0.0001665045],"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":[0.00001311843,0.000009607545,0.0009831322,0.000009829445,0.00001067778,0.0000106185,0.00001927898,0.9944257,0.0001176987,0.002663545,0.00005464312,0.001682189],"study_design_scores_gemma":[0.000003801762,0.00000500534,0.0000938243,0.000001408069,0.000001501681,0.000002141303,0.000002702752,0.9980854,0.00006599702,0.001689117,0.00004738217,0.000001719585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3188741,0.0001874468,0.6782298,0.0003240844,0.00002981583,0.00007804127,0.0001716731,0.0003852872,0.001719855],"genre_scores_gemma":[0.8612753,0.00008079688,0.1375307,0.00009296082,0.00001809036,0.0001943064,0.0001715387,0.0000797691,0.0005565688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009643817,"threshold_uncertainty_score":0.02498507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03561721250737918,"score_gpt":0.2552968074608934,"score_spread":0.2196795949535142,"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."}}