{"id":"W3161180470","doi":"","title":"Target analysis and suspect screening of wastewater derived contaminants in receiving riverine and coastal areas and assessment of environmental risks","year":2020,"lang":"en","type":"dissertation","venue":"Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Regional Development Fund; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Generalitat de Catalunya; Ministerio de Economía y Competitividad; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja; Svenska Forskningsrådet Formas; Canadian Institute for Advanced Research","keywords":"Contamination; Environmental chemistry; Environmental science; Wastewater; Organism; Nonylphenol; Environmental risk assessment; Pollutant; Aquatic environment; Ecosystem; Sewage treatment; Risk assessment; Chemistry; Environmental engineering; Biology; Ecology; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009800264,0.000630756,0.0005125111,0.001348661,0.0004764893,0.001402494,0.0005281679,0.001094752,0.001203879],"category_scores_gemma":[0.001232646,0.0003522112,0.0006301354,0.0008273488,0.0003839479,0.0005012175,0.0007735378,0.0006306448,0.0005264588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00052688,"about_ca_system_score_gemma":0.001126483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251061,"about_ca_topic_score_gemma":0.002134959,"domain_scores_codex":[0.9986262,0.0001587659,0.00008470503,0.0002581684,0.0007805101,0.00009163898],"domain_scores_gemma":[0.9994242,0.00009679714,0.0001290942,0.00003272324,0.0002931082,0.00002401989],"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":[0.0002683839,0.0002652584,0.01638539,0.001106926,0.00007510238,0.0003766999,0.0005050232,0.003798362,0.8963994,0.001247026,0.0008150992,0.07875727],"study_design_scores_gemma":[0.00001925032,0.001166162,0.01444822,0.0001440185,0.0001240988,0.0005892901,0.000808838,0.008073599,0.961034,0.0007465459,0.01280025,0.00004572529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951259,0.003364343,0.08884354,0.0004121336,0.0001096317,0.0005210883,0.001189164,0.0003777355,0.01005655],"genre_scores_gemma":[0.8804393,0.007341815,0.09218222,0.0005290061,0.00003859033,0.0006159609,0.001114745,0.00009639624,0.01764203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001402494,"threshold_uncertainty_score":0.005182922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036788533364029,"score_gpt":0.2934617280145351,"score_spread":0.2730938426808948,"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."}}