{"id":"W4387521884","doi":"10.1139/er-2023-0042","title":"A multi-realm perspective on applying potential tipping points to environmental decision-making","year":2023,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Queen's University; Université Laval; The Scarborough Hospital; University of Toronto; Carleton University","funders":"Fisheries and Oceans Canada; Universidade Federal de Santa Catarina; Carleton University; Natural Sciences and Engineering Research Council of Canada; Wildlife Conservation Society","keywords":"Tipping point (physics); Realm; Environmental resource management; Predictability; Context (archaeology); Ecosystem management; Ecosystem; Ecology; Business; Environmental science; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007682154,0.0004344123,0.0004522216,0.000141796,0.0004873838,0.00006273599,0.0005948544,0.0001084194,0.001538067],"category_scores_gemma":[0.0001021177,0.0003768113,0.0002739898,0.0003720341,0.0001572927,0.0002164581,0.0008120735,0.0002744883,0.0428844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536764,"about_ca_system_score_gemma":0.000004622506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003753704,"about_ca_topic_score_gemma":0.00003895472,"domain_scores_codex":[0.99665,0.0001597966,0.0006123564,0.001104744,0.0007572855,0.0007158484],"domain_scores_gemma":[0.9987112,0.0001147288,0.0002064022,0.0006578659,3.505093e-7,0.000309502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002191488,0.001399693,0.05299646,0.00004968207,0.00008828328,0.0005446862,0.004882996,0.02201428,0.2282775,0.00008026922,0.006780563,0.6826664],"study_design_scores_gemma":[0.00238024,0.000875494,0.6296638,0.001850586,0.0001337727,0.000219228,0.007518089,0.1013294,0.0008426454,0.0010308,0.2508085,0.003347455],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851093,0.0009008989,0.006039761,0.0003017929,0.0005585871,0.003134629,0.00006642912,0.0001420846,0.00374651],"genre_scores_gemma":[0.9889832,0.001815369,0.00706195,0.0007820496,0.00008557452,0.0003242999,0.00002127467,0.00006521919,0.0008610316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.679319,"threshold_uncertainty_score":0.9998684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227783049223128,"score_gpt":0.2798755816106913,"score_spread":0.26759775111846,"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."}}