{"id":"W3134195029","doi":"10.1117/12.2577050","title":"Machine learning techniques for real-time UV-Vis spectral analysis to monitor dissolved nutrients in surface water","year":2021,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"AUG Signals (Canada)","funders":"","keywords":"Surface water; Support vector machine; Dissolved organic carbon; Mean squared error; Environmental science; Machine learning; Artificial neural network; Artificial intelligence; Computer science; Environmental chemistry; Environmental engineering; Mathematics; Chemistry; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004822271,0.0001710249,0.0003463092,0.0001056703,0.0001273966,0.00007852213,0.0001852478,0.00006455569,0.001298747],"category_scores_gemma":[0.00003511601,0.0001296717,0.0002242498,0.0008641902,0.00002873766,0.0001238103,0.0001898728,0.0001098817,0.0002300031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001877794,"about_ca_system_score_gemma":0.000002864326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005072542,"about_ca_topic_score_gemma":0.0001882111,"domain_scores_codex":[0.9983207,0.0001251183,0.0003167596,0.0005086667,0.0002832153,0.0004455266],"domain_scores_gemma":[0.9994707,0.00005214569,0.00003522974,0.0002816281,0.00001683591,0.0001435103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003167015,0.0001694337,0.3998675,0.000006889509,0.0001688466,0.00001414967,0.000703298,0.005648057,0.5926206,0.000005313671,0.000137369,0.0006268614],"study_design_scores_gemma":[0.0001757934,0.00005990691,0.02848138,0.000008778096,0.0001816113,5.126848e-7,0.0001527218,0.004986286,0.9641483,0.00008084237,0.001456363,0.0002674771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960066,0.000005454962,0.002110733,0.0007104337,0.00002903492,0.0001484083,0.00001212475,0.0001211138,0.0008561159],"genre_scores_gemma":[0.9603433,0.00002018,0.01951272,0.00002417721,0.00005088979,0.0000215957,0.0001032309,0.00001747698,0.01990646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3715278,"threshold_uncertainty_score":0.9996142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256241184540876,"score_gpt":0.2655851765163516,"score_spread":0.2530227646709428,"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."}}