{"id":"W4385202334","doi":"10.3390/environments10070129","title":"Using Aquatic Mesocosms to Assess the Effects of Soil and Vegetation for Informing Environmental Research","year":2023,"lang":"en","type":"article","venue":"Environments","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada's Oil Sands Innovation Alliance; Innotech Alberta; Shell Canada; Suncor Energy Incorporated; Canadian Natural Resources Limited","keywords":"Mesocosm; Macrophyte; Turbidity; Environmental science; Vegetation (pathology); Exclosure; Hydrology (agriculture); Aquatic plant; Periphyton; Ecology; Ecosystem; Algae; Grazing; Geology; Geotechnical engineering; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001278951,0.0006656554,0.0005380264,0.0003839975,0.001096971,0.0006356231,0.0007409927,0.0004833259,0.000916353],"category_scores_gemma":[0.0008382159,0.0003000717,0.000454915,0.0004794887,0.0005806252,0.0005835248,0.000855897,0.001199941,0.0001889705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001906073,"about_ca_system_score_gemma":0.001839282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03411693,"about_ca_topic_score_gemma":0.1392437,"domain_scores_codex":[0.9989017,0.0001428175,0.00006659991,0.0004692188,0.0003366743,0.00008303683],"domain_scores_gemma":[0.9988322,0.0001678248,0.0002771872,0.0001526413,0.0002982493,0.0002718568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001297024,0.002125854,0.1556815,0.0001905618,0.0001738797,0.0001795597,0.0006718116,0.00294887,0.8172128,0.0003434763,0.000563135,0.01861155],"study_design_scores_gemma":[0.0003820265,0.006324437,0.8140441,0.00004741696,0.0002367276,0.00009969671,0.0008923704,0.02088363,0.1497263,0.0009587086,0.006313366,0.00009124532],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857792,0.0001603439,0.01002653,0.00007943598,0.00004219694,0.0009765375,0.001273141,0.00007763086,0.001585005],"genre_scores_gemma":[0.9051004,0.0003579454,0.08509845,0.0005166486,0.00002908892,0.003817877,0.002049107,0.00005035672,0.002980103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03411693,"threshold_uncertainty_score":0.0678367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0573522986854498,"score_gpt":0.3263307586216079,"score_spread":0.2689784599361581,"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."}}