{"id":"W3202021264","doi":"10.18280/ts.380407","title":"Design of a Groundwater Level Monitoring System Based on Internet of Things and Image Recognition","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory of Hydroscience and Engineering; Tsinghua University","keywords":"Groundwater; Water level; Computer science; Process (computing); Real-time computing; Internet of Things; Data acquisition; Remote sensing; The Internet; Embedded system; Engineering; Geography","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":[0.0004538771,0.0001156358,0.0001659718,0.00004890198,0.00002863272,0.00002462149,0.0001385608,0.00005580358,0.0001394618],"category_scores_gemma":[0.00002257197,0.0001039285,0.00003359007,0.00009748124,0.0001244763,0.0001838699,0.0001143518,0.00008064182,0.00001589807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120571,"about_ca_system_score_gemma":0.00000642894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002092749,"about_ca_topic_score_gemma":6.235231e-7,"domain_scores_codex":[0.9988997,0.0001141153,0.0002925251,0.0002335515,0.0003115577,0.0001485721],"domain_scores_gemma":[0.9995865,0.00009572193,0.0001162103,0.0001516757,0.00002148665,0.00002837025],"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":[0.0001230024,0.0002608851,0.01741762,0.0002636022,0.00003283995,0.00002884241,0.001409522,0.0006519808,0.9672219,0.00003262725,0.00008758153,0.01246964],"study_design_scores_gemma":[0.0004232329,0.0002814003,0.01337147,0.0003141494,0.00002112452,0.000004280552,0.0004763381,0.005191958,0.9796214,0.0001731027,0.000009312477,0.0001122641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9031069,0.00000802395,0.09633604,0.00007225703,0.00007856726,0.0001582667,0.000005688993,0.00006913222,0.0001651681],"genre_scores_gemma":[0.9603164,0.000002140827,0.03959096,0.00001094643,0.00001726229,0.00001436841,0.000004441659,0.00001000157,0.00003346802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05720956,"threshold_uncertainty_score":0.423808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07334815147274283,"score_gpt":0.246775396790249,"score_spread":0.1734272453175061,"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."}}