{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002479044,0.0003788785,0.0004914398,0.0001387362,0.0002812113,0.000172232,0.0007991952,0.0007807984,0.02951906],"category_scores_gemma":[0.00002714739,0.0003805715,0.0001372133,0.00009185629,0.001205051,0.0002543959,0.0002132038,0.001301654,0.001897053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005602224,"about_ca_system_score_gemma":0.00002538353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005606695,"about_ca_topic_score_gemma":0.008331666,"domain_scores_codex":[0.9976976,0.0001451546,0.0003365073,0.0006199859,0.0002575014,0.0009432441],"domain_scores_gemma":[0.9989943,0.0001521055,0.0002899616,0.000496991,0.000004537141,0.00006208485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000005869761,0.00002581033,0.1202676,0.00002329374,0.00002507819,0.001353237,0.00005710524,0.000009575117,0.0005318522,0.0000210861,0.8775648,0.0001146759],"study_design_scores_gemma":[0.0008634711,0.00005511595,0.5108647,0.00004994475,0.00003762456,0.0001530401,0.001948605,0.00001339474,0.00006177392,0.0003060595,0.4848424,0.0008038601],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.5674521,0.00002951711,0.000003064248,0.4027004,0.0007363184,0.0002713277,0.00030369,0.00006502427,0.02843865],"genre_scores_gemma":[0.2476468,0.0002371054,0.0000422418,0.7491698,0.0004268293,0.0001334814,0.001532589,0.00006108053,0.0007501025],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.3927224,"threshold_uncertainty_score":0.9998646,"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."}}