{"id":"W4281259496","doi":"10.1111/ele.14028","title":"The role of demographic compensation in stabilising marginal tree populations in North America","year":2022,"lang":"en","type":"letter","venue":"Ecology Letters","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Alberta; University of Alberta; Agriculture Food and Rural Development; University of British Columbia","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Ecology; Range (aeronautics); Climate change; Population; Population size; Biology; Compensation (psychology); Population growth; Competition (biology); Geography; 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":[0.001098105,0.0001072057,0.0001747742,0.0002795564,0.0009188207,0.0006264013,0.0004558857,0.002279796,0.001545298],"category_scores_gemma":[0.003902941,0.0001201239,0.00007987567,0.0002423879,0.00106338,0.0005943706,0.0003316028,0.001094249,0.0005152711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008820738,"about_ca_system_score_gemma":0.0003846425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009509757,"about_ca_topic_score_gemma":0.03018574,"domain_scores_codex":[0.9997042,0.0001142534,0.00002012554,0.00006212514,0.00006230956,0.00003699999],"domain_scores_gemma":[0.9978217,0.001156573,0.0002256449,0.0001223141,0.0004422864,0.0002314974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007669358,0.0001874363,0.4985536,0.0002318265,0.00006382613,0.007153023,0.002422116,0.002628231,0.01529162,0.004091783,0.1860616,0.282548],"study_design_scores_gemma":[0.0000994613,0.0003507469,0.7648017,0.0002028975,0.00005180022,0.01028447,0.00423134,0.01194811,0.004076723,0.01616359,0.1876809,0.0001080994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6421793,0.003538541,0.001975251,0.3230908,0.001467744,0.00001799826,0.0002127073,0.0001443373,0.02737334],"genre_scores_gemma":[0.9345499,0.001408412,0.001056404,0.05506364,0.002497005,0.00002122784,0.00008201113,0.00002176966,0.005299609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9904903,"threshold_uncertainty_score":0.0189088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040402484723763,"score_gpt":0.2282712457238819,"score_spread":0.2078672208766443,"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."}}