{"id":"W4414440304","doi":"10.1111/csp2.70153","title":"Using rare mosses to resolve barriers in the use of species distribution models for climate change vulnerability assessments","year":2025,"lang":"en","type":"article","venue":"Conservation Science and Practice","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Alberta Museum; University of Alberta; Environment and Climate Change Canada; Royal British Columbia Museum; University of British Columbia","funders":"","keywords":"Climate change; Microclimate; Vulnerability (computing); Species distribution; Distribution (mathematics); Climate model; Extrapolation; Vulnerability assessment","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002766751,0.00007724915,0.00009660444,0.00004722005,0.0004397865,0.0001871992,0.0002050644,0.00003305208,0.0001632545],"category_scores_gemma":[0.004884048,0.00006185633,0.00001857299,0.001333556,0.0004525028,0.002527356,0.0001470163,0.0000605584,0.000002275295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000346526,"about_ca_system_score_gemma":0.00006795258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007655551,"about_ca_topic_score_gemma":0.0002794989,"domain_scores_codex":[0.9987735,0.0001398384,0.0002199939,0.0002712248,0.0003811755,0.0002142675],"domain_scores_gemma":[0.9984874,0.0009355509,0.0001216971,0.0002173653,0.0001830367,0.00005491059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002175359,0.0007361755,0.5966769,0.0003472841,0.00002812094,0.000006613416,0.01314246,0.004537812,0.05726599,0.2708111,0.03820121,0.01607099],"study_design_scores_gemma":[0.0006536264,0.0001300493,0.6419154,0.00007846853,0.00005649226,0.000007509908,0.02607794,0.06270807,0.001796626,0.001391067,0.2649215,0.0002631307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470168,0.00003469078,0.02822441,0.01854863,0.0001696808,0.001227379,0.0004672102,0.0000151722,0.00429598],"genre_scores_gemma":[0.9899745,0.0001051386,0.002376962,0.007363947,0.00000827493,0.0001038339,0.00003512528,0.000002461115,0.0000297356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.26942,"threshold_uncertainty_score":0.5847014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3661600315051171,"score_gpt":0.4278124930717909,"score_spread":0.06165246156667381,"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."}}