{"id":"W4409103531","doi":"10.2139/ssrn.5202830","title":"Guiding Assisted Migration Amidst Climate Change: Refining Site Selection for Sugar Maple with Fine- Scale Spatial Distribution Modeling","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Natural Resources Canada; University of British Columbia Hospital; Université Laval","funders":"","keywords":"Maple; Refining (metallurgy); Scale (ratio); Selection (genetic algorithm); Sugar; Climate change; Environmental science; Spatial distribution; Distribution (mathematics); Geography; Computer science; Materials science; Ecology; Mathematics; Remote sensing; Cartography; Biology; Metallurgy; Machine learning","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":[0.0009160859,0.0005641849,0.0009281757,0.0005651235,0.000543147,0.0006839283,0.001580237,0.0009858073,0.001297147],"category_scores_gemma":[0.003353992,0.0005869018,0.0006260074,0.0005671646,0.0004726068,0.0006894929,0.001003263,0.0007489132,0.0002148552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005000496,"about_ca_system_score_gemma":0.001291466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04369228,"about_ca_topic_score_gemma":0.04985709,"domain_scores_codex":[0.9997912,0.00008572267,0.000006537056,0.00005345395,0.000018808,0.00004426382],"domain_scores_gemma":[0.9985429,0.001042003,0.00008756851,0.00008877835,0.0001247093,0.0001139882],"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":[0.0000441298,0.0000414582,0.003309434,0.00001266064,0.0000246386,0.00003094903,0.00004583826,0.9835489,0.0003954662,0.0006566443,0.0003333793,0.01155643],"study_design_scores_gemma":[0.000004941913,0.000005809384,0.0002262309,9.300113e-7,0.000002219212,0.000002519714,0.000007538593,0.9992908,0.00004171455,0.0003726092,0.00004322563,0.000001381471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5119429,0.0002864518,0.4843213,0.0005124508,0.00005881433,0.00006180199,0.0004211852,0.0007993651,0.00159557],"genre_scores_gemma":[0.9346035,0.00006526773,0.06378561,0.00007277142,0.00002425632,0.00004467769,0.0003659161,0.00006655172,0.0009714814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04369228,"threshold_uncertainty_score":0.08687598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05449706850316387,"score_gpt":0.2928235286823762,"score_spread":0.2383264601792123,"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."}}