{"id":"W3009818955","doi":"10.1002/ecy.3040","title":"Effect of time series length and resolution on abundance‐ and trait‐based early warning signals of population declines","year":2020,"lang":"en","type":"article","venue":"Ecology","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Universität Zürich; European Commission; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Directorate-General for Migration and Home Affairs; National Science Foundation","keywords":"Trait; Population; Warning system; Abundance (ecology); Series (stratigraphy); Ecology; Time series; Threatened species; Statistics; Econometrics; Environmental science; Computer science; Biology; Demography; Mathematics; Habitat","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":[0.005738329,0.0003852572,0.000332656,0.0003912986,0.0003163322,0.0008684058,0.000547359,0.0008329965,0.001097621],"category_scores_gemma":[0.02365419,0.0002743709,0.0005925376,0.0003992694,0.0005567048,0.00131032,0.0005422121,0.00114385,0.0001167945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000449011,"about_ca_system_score_gemma":0.0004385704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003258013,"about_ca_topic_score_gemma":0.002168387,"domain_scores_codex":[0.9992033,0.0003944601,0.00008478889,0.0001405604,0.000117545,0.00005933195],"domain_scores_gemma":[0.9761925,0.01931046,0.001623552,0.00147986,0.0008125892,0.0005810321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001687771,0.0004959218,0.0866302,0.0002923389,0.0004501544,0.0002798468,0.0002146116,0.8548964,0.01620809,0.002456618,0.0006171113,0.03577099],"study_design_scores_gemma":[0.00009153302,0.000854764,0.05086369,0.000101076,0.0002180856,0.0001312151,0.0001479371,0.9311539,0.01373654,0.001798757,0.0008332388,0.00006936106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881726,0.0004314534,0.009618547,0.000357103,0.00005595866,0.00002413784,0.0003054416,0.00009536426,0.0009392948],"genre_scores_gemma":[0.99589,0.0001161378,0.003571357,0.0000441703,0.000008488919,0.00001859764,0.0002346358,0.000009325958,0.0001071525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005738329,"threshold_uncertainty_score":0.03034759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004346016279933035,"score_gpt":0.2120231626133064,"score_spread":0.2076771463333733,"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."}}