{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006472008,0.00006787699,0.00007977328,0.00001018615,0.0001539977,0.00001757069,0.00004633793,0.00004460366,0.0001609173],"category_scores_gemma":[0.00001111313,0.00006493122,0.00001067017,0.00005592656,0.00006127373,0.0001242498,0.00005898221,0.00005233297,0.00004679615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003174007,"about_ca_system_score_gemma":0.000006502741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006929714,"about_ca_topic_score_gemma":0.01111061,"domain_scores_codex":[0.9994755,0.00002366887,0.00008588487,0.0001769654,0.0001073887,0.0001306245],"domain_scores_gemma":[0.9998099,0.00005463544,0.00003646036,0.00005065862,0.000005543401,0.00004275775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000777161,0.00004319291,0.9917527,0.00002337861,0.00003205028,0.00002297509,0.002061096,0.0001039451,0.001887341,0.0001099665,0.000284274,0.003671354],"study_design_scores_gemma":[0.0002144026,0.00001545291,0.9958003,0.000008030844,0.00001020317,0.00001601564,0.0002524052,0.001648406,0.0001554937,0.0008544039,0.0009505058,0.00007437926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938491,0.0001824468,0.00002856572,0.0003002129,0.0001413401,0.00004981429,0.00003477667,0.00002041731,0.005393307],"genre_scores_gemma":[0.9984028,0.0002474693,0.0002060566,0.0004167029,0.00003361146,0.000007706537,0.00004724814,0.000004115052,0.0006342372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01104131,"threshold_uncertainty_score":0.6199979,"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."}}