{"id":"W7042417563","doi":"","title":"Passive sampling to understand and predict sources of wastewater and agricultural contamination in rural watersheds","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs; University of Waterloo; Ministry of Environment","keywords":"Passive sampling; Sampling (signal processing); Sucralose; STREAMS; Range (aeronautics); Wastewater; Contamination; Surface water","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":[0.0003306636,0.0003058457,0.00032648,0.0009187685,0.0002115178,0.0006701103,0.000330456,0.0003874889,0.0005419521],"category_scores_gemma":[0.0005427793,0.0001736905,0.0002426827,0.001122675,0.0001770339,0.000466295,0.000370097,0.0002287229,0.0001986064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468968,"about_ca_system_score_gemma":0.000544739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009776704,"about_ca_topic_score_gemma":0.01506704,"domain_scores_codex":[0.9998079,0.00003289804,0.00001369108,0.0000650442,0.00005724829,0.00002328744],"domain_scores_gemma":[0.9997614,0.00007860293,0.00006464936,0.000009893918,0.00007135561,0.00001417208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002460796,0.0004124931,0.7510678,0.0003896749,0.0000995113,0.0002673976,0.000565051,0.0202339,0.1208515,0.000601773,0.0006406659,0.1046242],"study_design_scores_gemma":[0.00004938628,0.0007653106,0.7412346,0.0000704348,0.0001116817,0.0003066356,0.002234491,0.2010474,0.04443747,0.001963414,0.007738664,0.00004051328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773942,0.0002694924,0.01964639,0.00006618148,0.00000441658,0.0001259087,0.0009704461,0.0001239097,0.001398967],"genre_scores_gemma":[0.9747725,0.0004717369,0.02206174,0.00005184061,0.000007023656,0.0001068388,0.001138528,0.00001868597,0.001371181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009776704,"threshold_uncertainty_score":0.01943964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01373482054644275,"score_gpt":0.2069943053557341,"score_spread":0.1932594848092914,"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."}}