{"id":"W4402557937","doi":"10.1111/rec.14265","title":"How does restoration ecology consider climate change uncertainties in forested ecosystems?","year":2024,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation, Science and Economic Development Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Restoration ecology; Ecology; Climate change; Ecosystem; Environmental science; Ecosystem ecology; Forest restoration; Forest ecology; Environmental resource management; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004627625,0.000178458,0.0002146268,0.0002847094,0.0001325161,0.0001701234,0.0001502871,0.0002037327,0.001412019],"category_scores_gemma":[0.0001053386,0.0001470333,0.00004536753,0.0004134082,0.0001725373,0.0009264633,0.0001361134,0.0001578801,0.00172064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005152181,"about_ca_system_score_gemma":0.00002801129,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007180866,"about_ca_topic_score_gemma":0.1338641,"domain_scores_codex":[0.9984484,0.0002570964,0.0003035822,0.0003974243,0.0001574128,0.0004360987],"domain_scores_gemma":[0.9994359,0.0001530629,0.0001054186,0.0002311657,0.00001323408,0.00006121035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008210969,0.0001192898,0.8283609,0.000263484,0.00003806063,0.0002032979,0.002918322,0.001948512,0.0006769249,0.07692853,0.0843673,0.004093316],"study_design_scores_gemma":[0.0007761877,0.0004827374,0.4833949,0.00006442588,0.00004512903,0.00002432778,0.0004049556,0.06463427,0.00009552063,0.006416952,0.4431001,0.0005605383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753564,0.0001115087,0.00003211839,0.01515747,0.002115822,0.0009413015,0.00001839328,0.0001945738,0.006072405],"genre_scores_gemma":[0.9948142,0.0001423159,0.00009759326,0.0005098165,0.0002722139,0.0003077018,0.00008382135,0.00002196516,0.003750343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3587328,"threshold_uncertainty_score":0.9995008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572456905394519,"score_gpt":0.268316200447745,"score_spread":0.2425916313937998,"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."}}