{"id":"W2624696656","doi":"10.1111/ele.12794","title":"Less favourable climates constrain demographic strategies in plants","year":2017,"lang":"en","type":"letter","venue":"Ecology Letters","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Horizon 2020 Framework Programme; Aarhus Universitets Forskningsfond; European Research Council; National Science Foundation; Science Foundation Ireland; Sight Research UK; Natural Environment Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Division of Arctic Sciences; Max-Planck-Institut für demografische Forschung; Aarhus Universitet","keywords":"Ecology; Climate change; Extinction (optical mineralogy); Resistance (ecology); Range (aeronautics); Population; Ecological niche; Vulnerability (computing); Biology; Geography; Habitat; Demography","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":[0.000720703,0.0001196622,0.0001708961,0.0002384846,0.0004648403,0.0007940499,0.0003304018,0.000940515,0.004088633],"category_scores_gemma":[0.003489564,0.0001012421,0.00008509149,0.0003968649,0.001093723,0.000985904,0.0007645473,0.0007151063,0.001071417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004801594,"about_ca_system_score_gemma":0.0002323033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001315261,"about_ca_topic_score_gemma":0.003697918,"domain_scores_codex":[0.9997209,0.00009739487,0.00002024639,0.00006834767,0.00003996483,0.00005313863],"domain_scores_gemma":[0.9981582,0.000678849,0.0006137992,0.0001794309,0.0001878862,0.00018197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003504542,0.0001023349,0.8126588,0.0001645253,0.00005801404,0.00213357,0.002463799,0.003585206,0.03352831,0.01654282,0.01282081,0.1155914],"study_design_scores_gemma":[0.00003087813,0.0001026254,0.8966486,0.0000774695,0.00002582515,0.004082409,0.002272082,0.005228102,0.003138241,0.04207595,0.04626648,0.00005132825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.955715,0.001522021,0.003921382,0.01722499,0.0001338326,0.00001039317,0.0002646263,0.00007241133,0.02113532],"genre_scores_gemma":[0.9954967,0.0005633147,0.0004448658,0.002319753,0.0002175821,0.000006883385,0.00008339663,0.00001289165,0.0008546813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004088633,"threshold_uncertainty_score":0.01367784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02897420287225064,"score_gpt":0.2479973356496019,"score_spread":0.2190231327773513,"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."}}