{"id":"W2613077560","doi":"10.1111/avsc.12317","title":"Fix‐it Felix: advances in testing plant facilitation as a restoration tool","year":2017,"lang":"en","type":"article","venue":"Applied Vegetation Science","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Facilitation; Dependency (UML); Context (archaeology); Vegetation (pathology); Restoration ecology; Ecology; Environmental resource management; Psychology; Biology; Computer science; Geography; Environmental science; Archaeology; Artificial intelligence; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.03085278,0.0007060186,0.001269608,0.001224921,0.001500313,0.002336709,0.002841525,0.001968055,0.007032257],"category_scores_gemma":[0.04626433,0.0002957515,0.0009082271,0.0009434822,0.004885918,0.003839464,0.0033642,0.002923008,0.0008303805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247662,"about_ca_system_score_gemma":0.001706652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005124254,"about_ca_topic_score_gemma":0.008768115,"domain_scores_codex":[0.9907232,0.00512966,0.0002667394,0.00121326,0.002361607,0.0003054728],"domain_scores_gemma":[0.9260048,0.05921242,0.004363765,0.005436756,0.003889072,0.00109321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002117207,0.001230322,0.08886873,0.002747558,0.001444272,0.0003401373,0.002945251,0.01009599,0.0108897,0.06285448,0.02108474,0.7953816],"study_design_scores_gemma":[0.0005449932,0.008309094,0.2353972,0.005218772,0.002844708,0.001622404,0.005945327,0.06410617,0.03556424,0.2458797,0.393709,0.0008583904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4900883,0.06650434,0.2847117,0.04313434,0.005792825,0.0006230487,0.001443649,0.001247463,0.1064543],"genre_scores_gemma":[0.831035,0.009235451,0.1450206,0.006352877,0.001651655,0.0005382567,0.0005510983,0.0005328576,0.005082343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03085278,"threshold_uncertainty_score":0.1631671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02466682062961076,"score_gpt":0.293833722398299,"score_spread":0.2691669017686882,"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."}}