{"id":"W2547121633","doi":"10.1073/pnas.1604978113","title":"Early warning signals of regime shifts in coupled human–environment systems","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":189,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; James S. McDonnell Foundation","keywords":"Warning system; Tipping point (physics); Regime shift; Ecosystem; Variety (cybernetics); Early warning system; SIGNAL (programming language); Complex system; Environmental resource management; Environmental science; State (computer science); Computer science; Ecology; Telecommunications; Engineering; Artificial intelligence; Biology","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":[0.001036514,0.0003562659,0.0003558316,0.0006015016,0.0003408161,0.001095817,0.0004784705,0.0006825491,0.00176168],"category_scores_gemma":[0.005687989,0.0002742127,0.0004422496,0.0002599363,0.001406494,0.001874186,0.001382296,0.0009126959,0.00006453563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008388518,"about_ca_system_score_gemma":0.0003364008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004648544,"about_ca_topic_score_gemma":0.003240875,"domain_scores_codex":[0.99967,0.0001423519,0.00001454222,0.00008274175,0.00004118772,0.00004905841],"domain_scores_gemma":[0.9975694,0.001423652,0.0006443484,0.0001193504,0.00007966607,0.0001636662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002240571,0.0001076711,0.05052613,0.00009271842,0.0002343807,0.0003719439,0.0007539374,0.872122,0.007456774,0.0540606,0.001022411,0.01302738],"study_design_scores_gemma":[0.00001773301,0.00004913188,0.0164541,0.00001538172,0.00002338517,0.00005902582,0.0002020309,0.9525083,0.0004097708,0.02982477,0.0004036842,0.00003278461],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365091,0.0001279629,0.05878065,0.0004865356,0.00002253219,0.00002032818,0.000117018,0.0001220308,0.003813826],"genre_scores_gemma":[0.9985399,0.00002927384,0.001131578,0.0000223331,0.00000413616,0.000005145258,0.00001385305,0.00000468305,0.0002490261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004648544,"threshold_uncertainty_score":0.009242952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041655777284664,"score_gpt":0.2536801567132843,"score_spread":0.2332635989404377,"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."}}