{"id":"W2064276962","doi":"10.3390/f4030613","title":"Managing Understory Vegetation for Maintaining Productivity in Black Spruce Forests: A Synthesis within a Multi-Scale Research Model","year":2013,"lang":"en","type":"article","venue":"Forests","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Université de Sherbrooke; Université Laval; Centre de Géomatique du Québec; Ministère des Ressources naturelles et des Forêts (Québec); Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Understory; Black spruce; Silviculture; Boreal; Taiga; Environmental science; Ecology; Forest management; Ecosystem; Biodiversity; Vegetation (pathology); Agroforestry; Productivity; Forest ecology; Disturbance (geology); Biome; Geography; Biology; Canopy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001093061,0.0007044835,0.000575926,0.0008607103,0.0005672354,0.002246721,0.001085388,0.0007436125,0.001353443],"category_scores_gemma":[0.001116924,0.000221039,0.0006504615,0.001083244,0.0007381204,0.001727635,0.0007012352,0.0006624711,0.0001323448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004642866,"about_ca_system_score_gemma":0.00633839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07724822,"about_ca_topic_score_gemma":0.1459576,"domain_scores_codex":[0.9997739,0.00007755231,0.0000159524,0.00005198038,0.00004307468,0.00003760775],"domain_scores_gemma":[0.9994938,0.000275251,0.00006438401,0.00002663805,0.00008196628,0.0000579374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001129556,0.0003116609,0.01724197,0.007587138,0.0003877223,0.0003532208,0.001140896,0.519062,0.004196598,0.2232304,0.005388297,0.2209871],"study_design_scores_gemma":[0.00007150313,0.0006289225,0.03018412,0.00620537,0.001263521,0.000194001,0.003134762,0.6398941,0.002168746,0.160455,0.155651,0.0001490549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2248419,0.3989004,0.251753,0.02064307,0.0007833498,0.0005788447,0.001529696,0.0004616248,0.100508],"genre_scores_gemma":[0.7489169,0.1895178,0.05588405,0.0007354154,0.000212767,0.0002833885,0.0003071812,0.00004297899,0.004099587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07724822,"threshold_uncertainty_score":0.1535972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0502196336793203,"score_gpt":0.2996276269255709,"score_spread":0.2494079932462506,"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."}}