{"id":"W2904099339","doi":"10.1016/j.scitotenv.2018.12.238","title":"A modelling approach to assess the impact of land mining on marine biodiversity: Assessment in coastal catchments experiencing catastrophic events (SW Brazil)","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Centre for Biological Sciences; Natural Sciences and Engineering Research Council of Canada; Mitacs; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Deutsche Forschungsgemeinschaft; National Oceanic and Atmospheric Administration; University of Victoria","keywords":"Environmental science; Disturbance (geology); Ecosystem; Biodiversity; Marine ecosystem; Sediment; Impact assessment; Marine protected area; Land use; Hydrology (agriculture); Environmental resource management; Ecology; Geology","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":[0.00106285,0.0001925166,0.0001786578,0.00006634743,0.0004017666,0.00002783005,0.001233014,0.00002008033,0.0002051396],"category_scores_gemma":[0.00001296111,0.00009689592,0.0001023344,0.0005181789,0.001142076,0.0001662738,0.00765357,0.0001402597,0.00003704317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776905,"about_ca_system_score_gemma":0.00003395623,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007139718,"about_ca_topic_score_gemma":0.00003773433,"domain_scores_codex":[0.997762,0.00009136672,0.0002740192,0.0004147702,0.001035316,0.0004225005],"domain_scores_gemma":[0.9989607,0.00003366258,0.0001849629,0.0007214445,0.000006218771,0.00009305984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005663952,0.0003287692,0.02044979,0.000004047913,0.00001647391,4.972539e-7,0.002372334,0.9686435,0.005993656,0.00003187019,0.00007422945,0.002028194],"study_design_scores_gemma":[0.0007400009,0.000871849,0.7827494,0.00004985359,0.00004243626,0.000009450381,0.002801156,0.2065358,0.005482346,0.0003572956,0.00003412987,0.0003262972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895273,0.000001231867,0.0006707853,0.0003352228,0.00008434108,0.0005881267,0.00001612809,0.000004336222,0.008772555],"genre_scores_gemma":[0.9985226,0.000004450001,0.001063615,0.00002917051,0.00001535668,0.00002273192,0.000002070031,0.000005958065,0.0003340648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7622995,"threshold_uncertainty_score":0.9994718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464889110589432,"score_gpt":0.2569529830921338,"score_spread":0.2323040919862395,"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."}}