{"id":"W4386291856","doi":"10.5539/cis.v16n3p30","title":"Drawbacks of Traditional Environmental Monitoring Systems","year":2023,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); Quality (philosophy); Sample (material); Risk analysis (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004067209,0.00005407242,0.00006126787,0.0001231422,0.0001486869,0.00008250473,0.0002634819,0.00002360981,0.00001064338],"category_scores_gemma":[0.00001341188,0.000048089,0.00001168474,0.000408328,0.0005777611,0.003024407,0.0003300926,0.00004403771,0.0002048109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005875405,"about_ca_system_score_gemma":0.000005876889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001378795,"about_ca_topic_score_gemma":1.593125e-8,"domain_scores_codex":[0.9991418,0.000007573026,0.0001908036,0.00009818107,0.000418984,0.0001426883],"domain_scores_gemma":[0.9997266,0.00003047316,0.00006421699,0.0001316639,0.000004965248,0.00004207217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001416797,0.0000928542,0.6588312,0.000134137,0.00001485938,0.00000346172,0.01334165,0.04767255,0.05598857,0.01236506,0.004342027,0.2071995],"study_design_scores_gemma":[0.0001351843,0.00006577335,0.9363378,0.00002095229,0.00000146225,0.000009648737,0.0004904558,0.04423018,0.0151914,0.0003580421,0.003040451,0.0001186625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957042,0.000004242836,0.002759504,0.00005401015,0.0005157725,0.00007297645,0.000007360117,0.0001109904,0.0007709427],"genre_scores_gemma":[0.9981638,0.00002208036,0.001749042,0.000009488596,0.00003311545,0.000005435108,0.000003586274,0.000001280903,0.00001217102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2775066,"threshold_uncertainty_score":0.2632498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03117473113781925,"score_gpt":0.2474315175723209,"score_spread":0.2162567864345016,"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."}}