{"id":"W2058947572","doi":"10.1016/j.trac.2010.02.005","title":"Designing monitoring programs for water quality based on experience in Canada II. Characterization of problems and data-quality objectives","year":2010,"lang":"en","type":"article","venue":"TrAC Trends in Analytical Chemistry","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Parks Canada; Simon Fraser University; Environment and Climate Change Canada; ASL Environmental Sciences (Canada)","funders":"","keywords":"Process (computing); Quality (philosophy); Computer science; Data quality; Management science; Process management; Data science; Engineering; Operations management; Metric (unit)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006221175,0.000113147,0.0001894252,0.00002044208,0.00004736109,0.00001690282,0.0001910749,0.00006681805,0.0001887564],"category_scores_gemma":[0.00004975403,0.0000931691,0.00002051289,0.0001449829,0.0001291599,0.0001378381,0.00008401891,0.0001880426,3.113987e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745854,"about_ca_system_score_gemma":0.00003512435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08212488,"about_ca_topic_score_gemma":0.06785192,"domain_scores_codex":[0.9986848,0.00005347329,0.0004070739,0.0003676268,0.0002529044,0.0002340854],"domain_scores_gemma":[0.9994743,0.00006841055,0.00007671685,0.0002942784,0.00000662074,0.00007964558],"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":[0.0000555831,0.0002517477,0.3260814,0.00008787792,0.000003673869,0.00000105202,0.0008776195,0.0002449966,0.6648064,0.000010108,0.000002235262,0.007577304],"study_design_scores_gemma":[0.0004466605,0.00002010299,0.5170709,0.00003247061,0.00000484461,3.384853e-7,0.000192965,0.009671319,0.4722128,0.00003114954,0.0001575796,0.0001588233],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988077,6.413495e-7,0.000292505,0.000242737,0.00003253316,0.00009146456,0.00004379164,0.000007985757,0.0004806357],"genre_scores_gemma":[0.9988712,5.491856e-7,0.0007949777,0.00001784649,0.00001825975,0.00003604553,0.0001348702,0.000005313424,0.0001209416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1925936,"threshold_uncertainty_score":0.9491574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030621661165472,"score_gpt":0.3645468782612932,"score_spread":0.2614847121447459,"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."}}