{"id":"W4417430061","doi":"10.1111/csp2.70171","title":"Integrating climate‐change exposure and refugia into landscape planning: A practical guide","year":2025,"lang":"en","type":"article","venue":"Conservation Science and Practice","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Telus (Canada); Geoscience BC; Government of British Columbia; Alberta Energy; Université Laval; University of Alberta; Natural Resources Canada; Parks Canada; Canadian Forest Service","funders":"Natural Resources Canada; Environment and Climate Change Canada; Wilburforce Foundation","keywords":"Resource (disambiguation); Function (biology); Landscape planning; Landscape assessment; Climate change; Landscape connectivity; Land use; Spatial planning; Landscape epidemiology","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":[],"consensus_categories":[],"category_scores_codex":[0.002186976,0.0001066555,0.0001025066,0.00006948736,0.0005712485,0.0003210813,0.0001298951,0.00006014613,0.0009110484],"category_scores_gemma":[0.006048148,0.00009263024,0.00001138004,0.0008652036,0.0006088332,0.002223878,0.0003149177,0.0001453241,0.00006503426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001445656,"about_ca_system_score_gemma":0.00005658566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003121774,"about_ca_topic_score_gemma":0.0001198562,"domain_scores_codex":[0.9986989,0.00009592746,0.0002152637,0.0003734558,0.0003737402,0.0002427474],"domain_scores_gemma":[0.998723,0.0007379567,0.000133967,0.0001728229,0.0001241741,0.00010803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003803249,0.0002089604,0.6353487,0.00009307721,0.00001896784,0.00004787915,0.00852533,0.000001290738,0.04079591,0.1722619,0.09885436,0.04346328],"study_design_scores_gemma":[0.000416051,0.00009441762,0.2807891,0.00005681705,0.0000368954,0.0001012748,0.0259468,0.000916489,0.0005987568,0.0004343246,0.6904082,0.000200821],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6123189,0.0005275665,0.0008082056,0.08242951,0.0002751033,0.0003719204,0.000009141409,0.00008929452,0.3031703],"genre_scores_gemma":[0.9753804,0.0004768832,0.004463336,0.01928009,0.00002877084,0.00005175409,0.00001081274,0.000005423883,0.0003025357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5915539,"threshold_uncertainty_score":0.9975341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07520490762712051,"score_gpt":0.3834639124473132,"score_spread":0.3082590048201927,"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."}}