{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000197581,0.00005263627,0.000146754,0.0000151264,0.00004197287,0.000003015849,0.00003722878,0.00004638553,0.00005775917],"category_scores_gemma":[0.00007645695,0.00004242422,0.00001409352,0.00005432802,0.00009354328,0.00005819876,0.00002160294,0.00003486123,0.000004786885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001250139,"about_ca_system_score_gemma":0.000002213556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001198115,"about_ca_topic_score_gemma":0.0001453371,"domain_scores_codex":[0.9995034,0.0000940832,0.000135619,0.0001318155,0.00005874499,0.00007635699],"domain_scores_gemma":[0.9996692,0.0001596875,0.0000972618,0.00004058618,0.000003112895,0.00003014807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002309532,0.00001792283,0.8954577,0.0001158878,0.000006110135,0.000001750295,0.0003319212,0.02059911,0.08038617,0.00006326383,0.00001617061,0.002772984],"study_design_scores_gemma":[0.0002897038,0.002093842,0.90794,0.00001866137,0.000008562584,0.000001435023,0.000006971739,0.08460953,0.004913194,0.0000473708,0.00002147742,0.00004928924],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993875,0.00001903085,0.00007063472,0.0001806069,0.00001915141,0.0001290647,0.000005529317,0.000006707386,0.0001817481],"genre_scores_gemma":[0.9997347,0.000005760525,0.0001910244,0.0000289523,0.000007572502,0.000003187339,0.00000358805,0.000003436623,0.00002179042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07547297,"threshold_uncertainty_score":0.1730009,"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."}}