{"id":"W4405475284","doi":"10.3390/d16120763","title":"Habitat Suitability in the Eyes of the Beholder: Using Random Forest Models to Predict Land Cover Type and Scale of Selection Through Avian Functional Traits","year":2024,"lang":"en","type":"article","venue":"Diversity","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Bird Studies Canada; Association of Field Ornithologists; Ministry of Natural Resources","keywords":"Land cover; Habitat; Selection (genetic algorithm); Scale (ratio); Forest cover; Ecology; Random forest; Cover (algebra); Geography; Biology; Land use; Cartography; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001647051,0.00003953893,0.00005787363,0.00000795157,0.0001124005,0.000009716112,0.00007203984,0.00002424169,0.0008874171],"category_scores_gemma":[0.00001322614,0.00002516215,0.0000276369,0.0002460624,0.0001216211,0.0001403701,0.0001563538,0.00004447075,0.000005365904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009959471,"about_ca_system_score_gemma":0.000006218385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106926,"about_ca_topic_score_gemma":0.003173804,"domain_scores_codex":[0.9995589,0.00003644122,0.00006133982,0.0001021087,0.0001735559,0.00006760681],"domain_scores_gemma":[0.9998689,0.00003981138,0.000015865,0.0000505449,0.00001106134,0.00001379409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006626897,0.0000372626,0.9925846,0.00001348197,0.000004312229,1.717819e-7,0.00205856,0.003948706,0.00047164,0.0001113391,0.0006612311,0.00004240036],"study_design_scores_gemma":[0.0002548166,0.00002261344,0.9951761,0.000008785062,0.00001670712,0.000001418255,0.0006630691,0.003043689,0.0002012945,0.0004199744,0.0001631689,0.00002833575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976541,0.00002789202,0.0004396101,0.000143351,0.00006697514,0.0001496913,0.0001203854,0.000005169341,0.001392759],"genre_scores_gemma":[0.999896,0.000007341816,0.00001129994,0.00004851996,0.000005746574,8.124984e-7,0.000005483437,0.000001210536,0.00002352989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002591501,"threshold_uncertainty_score":0.9716595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05612200768678101,"score_gpt":0.2379907743863145,"score_spread":0.1818687666995335,"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."}}