{"id":"W4386714803","doi":"10.1101/2023.09.11.556540","title":"Winners and losers under past and future climate change","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Climate change; Niche; Range (aeronautics); Ecological niche; Ecology; Context (archaeology); Habitat; Occupancy; Geography; Global change; Species distribution; Extant taxon; Biology; Evolutionary biology","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":[0.0008197505,0.0001796612,0.0004502385,0.0005713892,0.0005707067,0.001439636,0.0002520581,0.0003577848,0.006539253],"category_scores_gemma":[0.002162163,0.00009519752,0.0002082767,0.0003741275,0.0004367814,0.0006624215,0.0007503304,0.0005429335,0.0006551507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003278699,"about_ca_system_score_gemma":0.0001056896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001763611,"about_ca_topic_score_gemma":0.003052844,"domain_scores_codex":[0.9997025,0.00005250216,0.00001297148,0.000095123,0.00006939107,0.00006748196],"domain_scores_gemma":[0.9990141,0.0002515757,0.0002328637,0.0001089853,0.0001132215,0.0002791472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00192098,0.0002811408,0.8718155,0.0001652765,0.0005392534,0.0005090654,0.001489408,0.01123133,0.03416128,0.00612325,0.006006654,0.06575686],"study_design_scores_gemma":[0.00002519321,0.0002272977,0.9696696,0.00001805251,0.00009619216,0.0002880001,0.002585053,0.01399785,0.00215017,0.006860446,0.004035536,0.00004667666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964625,0.0001097847,0.0004060841,0.000140729,0.00002856884,0.00000282455,0.0001725099,0.00001371555,0.002663212],"genre_scores_gemma":[0.998773,0.00004740202,0.0001353653,0.0000319537,0.00001223028,0.000002593099,0.0001592023,0.000008251892,0.0008298327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006539253,"threshold_uncertainty_score":0.02187598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896233728453439,"score_gpt":0.2266766834254521,"score_spread":0.1977143461409177,"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."}}