{"id":"W3109498698","doi":"10.1002/ecm.1470","title":"A guide to state–space modeling of ecological time series","year":2021,"lang":"en","type":"preprint","venue":"Ecological Monographs","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of British Columbia; University of Toronto; Fisheries and Oceans Canada","funders":"Banff International Research Station for Mathematical Innovation and Discovery; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Categorical variable; Flexibility (engineering); Range (aeronautics); Variety (cybernetics); Population; Data science; State space; Ecology; Machine learning; Data mining; Artificial intelligence; Mathematics; Statistics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002725817,0.002059551,0.001467953,0.002246948,0.0004567646,0.002335924,0.002649406,0.002974115,0.03731709],"category_scores_gemma":[0.008517945,0.001302236,0.002470379,0.002921634,0.0008627775,0.002512248,0.001360867,0.004975825,0.0210871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100939,"about_ca_system_score_gemma":0.001943088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006075791,"about_ca_topic_score_gemma":0.006324077,"domain_scores_codex":[0.9990395,0.0003634842,0.0001453532,0.0001640077,0.0002480113,0.00003970154],"domain_scores_gemma":[0.9956234,0.003267109,0.0001637502,0.0002664253,0.000595339,0.00008398296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004528093,0.0001136188,0.0007866403,0.001947237,0.0002018875,0.0007136791,0.0003268865,0.09945063,0.002370901,0.4251889,0.2366623,0.232192],"study_design_scores_gemma":[0.0000320415,0.00004084958,0.0004079679,0.0007379595,0.00003534091,0.0003602597,0.00004075456,0.1687601,0.000519752,0.2843965,0.5445799,0.00008850352],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003123778,0.008711534,0.9712185,0.001435427,0.0006540084,0.0001207882,0.003489648,0.002165369,0.01189243],"genre_scores_gemma":[0.01092689,0.02491367,0.9224289,0.001631645,0.001139661,0.001287731,0.006730399,0.001996233,0.02894484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03731709,"threshold_uncertainty_score":0.1248381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02715464857729526,"score_gpt":0.2655463708610619,"score_spread":0.2383917222837666,"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."}}