{"id":"W3198191675","doi":"10.3390/plants10091824","title":"Willow Aboveground and Belowground Traits Can Predict Phytoremediation Services","year":2021,"lang":"en","type":"article","venue":"Plants","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Phytoremediation; Willow; Biology; Environmental science; Environmental remediation; Agronomy; Agroforestry; Ecology; Soil water; Contamination","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":[0.0006949466,0.0003910901,0.0003501673,0.0005125324,0.0002745847,0.0005634894,0.0001855968,0.0003256494,0.0009062406],"category_scores_gemma":[0.0008585296,0.000128717,0.0002602574,0.0003499113,0.0002192704,0.0005142292,0.0004088277,0.0004268098,0.0002060878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003232096,"about_ca_system_score_gemma":0.0002681623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791223,"about_ca_topic_score_gemma":0.006329287,"domain_scores_codex":[0.9997635,0.00007011864,0.00001414406,0.00008128894,0.00003854508,0.00003234842],"domain_scores_gemma":[0.9989228,0.000428258,0.000253238,0.0001063068,0.0001102162,0.0001790585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006885824,0.0002583423,0.6095086,0.00009475357,0.0002185319,0.00007274104,0.0002750308,0.001118499,0.3713797,0.000199158,0.0001160769,0.01606995],"study_design_scores_gemma":[0.00000350556,0.0002376477,0.9913693,0.000003761921,0.00003423225,0.00002817529,0.00007598416,0.001932171,0.005881289,0.0001114885,0.00031608,0.000006332402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984902,0.000118301,0.000946873,0.00001139289,0.000001687234,0.000008074025,0.0001663364,0.0000103601,0.0002468239],"genre_scores_gemma":[0.9984218,0.00005606601,0.0009784639,0.00001724499,0.000001482145,0.000009633328,0.0002575215,0.000008791411,0.0002490213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002791223,"threshold_uncertainty_score":0.005549967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005353430612106485,"score_gpt":0.1966838085753027,"score_spread":0.1913303779631962,"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."}}