{"id":"W2910500871","doi":"10.3389/fpls.2018.01964","title":"Early-Warning Signals of Individual Tree Mortality Based on Annual Radial Growth","year":2019,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":192,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; University of Alberta; Université Laval; Natural Resources Canada; Centre de Géomatique du Québec; University of Victoria","funders":"Horizon 2020; Vlaamse regering; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Institució Catalana de Recerca i Estudis Avançats; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Fonds Wetenschappelijk Onderzoek; Javna Agencija za Raziskovalno Dejavnost RS; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Research, Development and Innovation Office; National Science Foundation","keywords":"Autocorrelation; Gymnosperm; Biology; Dendrochronology; Tree (set theory); Variance (accounting); Ecology; Metric (unit); Demography; Statistics; Mathematics; Botany; Economics","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":[0.0007761395,0.000248846,0.0002421386,0.001126668,0.0001548701,0.0003501473,0.0002345308,0.0001662823,0.0009567469],"category_scores_gemma":[0.001729667,0.00008374947,0.0002139345,0.0007466524,0.0001251627,0.0003834959,0.0003613814,0.0002751531,0.0002306498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383521,"about_ca_system_score_gemma":0.0001165469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280083,"about_ca_topic_score_gemma":0.004043249,"domain_scores_codex":[0.9997699,0.00005579413,0.00002098127,0.0000748653,0.00004885955,0.00002959059],"domain_scores_gemma":[0.9981167,0.0006172928,0.0007900908,0.0001568852,0.0001883989,0.0001306907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002523199,0.0000487456,0.9412625,0.00008107474,0.0001257032,0.0001070163,0.0001924296,0.003040796,0.02489177,0.0003083665,0.0003265552,0.0293628],"study_design_scores_gemma":[0.000002583116,0.00006561768,0.9909224,0.000006600433,0.00002606169,0.000101781,0.00006500238,0.006441296,0.001858337,0.0001795968,0.0003217776,0.000009053111],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926535,0.0002113744,0.005621742,0.00001579264,0.000007592766,0.000007392502,0.0007856927,0.00007815634,0.0006187485],"genre_scores_gemma":[0.9966123,0.00006756262,0.002254487,0.000006268059,0.000009669335,0.000007133236,0.0008704907,0.000009276287,0.0001629186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001280083,"threshold_uncertainty_score":0.004104674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005462900890502592,"score_gpt":0.1999793509813989,"score_spread":0.1945164500908963,"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."}}