{"id":"W2972719272","doi":"10.1016/j.plrev.2019.09.004","title":"Long transients in ecology: Theory and applications","year":2019,"lang":"en","type":"review","venue":"Physics of Life Reviews","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":185,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Russian Foundation for Basic Research; National Institute for Mathematical and Biological Synthesis","keywords":"Ecology; Anticipation (artificial intelligence); Population; Variety (cybernetics); Theoretical ecology; Ecological systems theory; Computer science; Biology; Sociology; Artificial intelligence","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":[0.001350233,0.001437092,0.002279685,0.003105238,0.0004098444,0.002046151,0.001464255,0.002269518,0.003864433],"category_scores_gemma":[0.002865382,0.0004009559,0.000598951,0.005016661,0.001473434,0.00326501,0.001497281,0.003016955,0.00139928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456938,"about_ca_system_score_gemma":0.00196015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547769,"about_ca_topic_score_gemma":0.002180242,"domain_scores_codex":[0.9996372,0.00008354917,0.00003911008,0.00007847918,0.0001304357,0.00003110328],"domain_scores_gemma":[0.9983674,0.001106369,0.0001410167,0.00005728779,0.0002438118,0.00008403616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004425441,0.00005510141,0.0001939381,0.015054,0.0001143248,0.00007385508,0.0001037811,0.001610064,0.0007229986,0.04328583,0.03534456,0.9033973],"study_design_scores_gemma":[0.0000203737,0.00005598439,0.0008272027,0.006004456,0.0001686675,0.0004526606,0.00008788166,0.0006318883,0.000253292,0.06814959,0.9232996,0.00004840629],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006197081,0.9980636,0.0004800537,0.0003466987,0.0002459153,0.000002658948,0.00001619508,0.000008266281,0.000774654],"genre_scores_gemma":[0.001028034,0.9971438,0.0004330351,0.0002526381,0.0006111561,0.000007987201,0.0000233649,0.000003368526,0.0004965469],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003864433,"threshold_uncertainty_score":0.01292783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03481916286230972,"score_gpt":0.3160609809909152,"score_spread":0.2812418181286055,"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."}}