{"id":"W4405202386","doi":"10.1111/rec.14353","title":"Regeneration lags and growth trajectories influence passive seismic line recovery in western North American boreal forests","year":2024,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Alberta-Pacific Forest Industries; Polar Knowledge Canada; Alberta Biodiversity Monitoring Institute; Alberta Conservation Association; Canadian Natural Resources Limited; Cenovus Energy; ConocoPhillips","keywords":"Regeneration (biology); Boreal; Taiga; Woodland caribou; Disturbance (geology); Deserts and xeric shrublands; Forest regeneration; Logging; Peat; Threatened species; Environmental science; Old-growth forest; Ecology; Geology; Habitat; Agroforestry; Paleontology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001821932,0.0001348263,0.0001811623,0.0001079025,0.00006493508,0.00006796787,0.00008453179,0.00007130539,0.00001368411],"category_scores_gemma":[0.0001311163,0.0001313079,0.00001896736,0.0004508636,0.0001606569,0.0006249463,0.00005091992,0.0001412354,0.0001120939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004082404,"about_ca_system_score_gemma":0.00004224288,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004338953,"about_ca_topic_score_gemma":0.2532341,"domain_scores_codex":[0.9988357,0.0001837483,0.0002629559,0.0003644763,0.0001362875,0.0002168611],"domain_scores_gemma":[0.9995177,0.0001788118,0.00009519945,0.0001318878,0.0000138009,0.00006263266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002403673,0.0000214104,0.9809375,0.00002831224,0.000006169548,0.00004083755,0.0003707254,0.008641334,0.0005936981,0.00004709639,0.0003637933,0.008925048],"study_design_scores_gemma":[0.0001304819,0.0004050692,0.9650646,0.00001402546,0.000006587694,0.00002235478,0.00001314088,0.0331857,0.0003480237,0.0001530592,0.0005244764,0.0001324248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978063,0.00003763302,0.0005234744,0.0008376533,0.0003092193,0.0003254462,0.00001250424,0.00007078031,0.00007696526],"genre_scores_gemma":[0.9992324,0.00006644063,0.0001058917,0.0002155365,0.00009237493,0.00009711359,0.00005048351,0.000016758,0.0001230409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2488951,"threshold_uncertainty_score":0.7603925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004769403384719513,"score_gpt":0.2195431698526668,"score_spread":0.2147737664679472,"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."}}