{"id":"W7125285633","doi":"10.53941/sai.2025.100001","title":"Sensors and AI","year":2025,"lang":"en","type":"article","venue":"Sensors and AI","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Dissemination; Multidisciplinary approach; Work (physics); Field (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009220417,0.00008319718,0.00009109364,0.00002931824,0.0001001835,0.00004164218,0.00007053805,0.00006544017,0.00003446399],"category_scores_gemma":[0.00003646143,0.00006928392,0.00001347349,0.00009897572,0.0002490791,0.00007295448,0.0002235147,0.0001089708,0.00004179321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002126122,"about_ca_system_score_gemma":0.000001736552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002455919,"about_ca_topic_score_gemma":0.00001019869,"domain_scores_codex":[0.9994432,0.00002245267,0.00008685105,0.0002129794,0.00007861347,0.0001558662],"domain_scores_gemma":[0.9997612,0.00002833319,0.00001461097,0.0001623591,0.000002730221,0.00003079474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003475142,0.00008768357,0.83835,0.00009070892,0.0000467173,0.00005802705,0.001526193,0.0004523676,0.04367721,0.005595724,0.04533787,0.06474277],"study_design_scores_gemma":[0.0007124737,0.0001203848,0.6019559,0.00009132556,0.00003875695,0.00002389443,0.001282215,0.001477931,0.1495087,0.03773886,0.206463,0.0005866034],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895938,0.00006471015,0.00001889184,0.008176668,0.00009451099,0.00006179477,0.000002498156,0.0001174172,0.001869696],"genre_scores_gemma":[0.9947633,0.00006354201,0.0005800916,0.0004038819,0.00001516815,0.000001944824,4.095788e-7,0.000004267742,0.004167354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2363941,"threshold_uncertainty_score":0.2825316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106509558331896,"score_gpt":0.2565911809493558,"score_spread":0.2455260853660368,"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."}}