{"id":"W4365788474","doi":"10.1109/incoft55651.2022.10094336","title":"Mobile Application for Water Management and Monitoring System in Residential Building","year":2022,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Rainwater harvesting; Water scarcity; Scarcity; Business; Environmental science; Water conservation; Farm water; Water resources; Clean water; Raw water; Economic shortage; Value (mathematics); Water resource management; Agriculture; Natural resource economics; Environmental engineering; Engineering; Waste management; Geography; Computer science; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003008176,0.0007658405,0.0005427408,0.0007510708,0.0003071697,0.0005017259,0.0009134254,0.0006842163,0.02275027],"category_scores_gemma":[0.0006109899,0.0001933228,0.0002854763,0.0004476178,0.0001116699,0.0006892225,0.0008049756,0.0003718128,0.006503101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002254557,"about_ca_system_score_gemma":0.000230416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00119466,"about_ca_topic_score_gemma":0.001176023,"domain_scores_codex":[0.999701,0.00004481863,0.00001924648,0.00006968329,0.0001143181,0.0000510346],"domain_scores_gemma":[0.9997571,0.00005648012,0.00002547787,0.00002742013,0.00009909461,0.00003447316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002110527,0.0009200862,0.01659948,0.001998704,0.0001481345,0.002623821,0.001373042,0.004385149,0.1345269,0.003651528,0.2231536,0.6085089],"study_design_scores_gemma":[0.0006394077,0.003296658,0.07244331,0.0006662431,0.0005430753,0.004627518,0.001311861,0.2013853,0.178369,0.00447338,0.5318415,0.000402657],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2535562,0.005731711,0.3614582,0.002665769,0.001302994,0.00399642,0.01898268,0.2180448,0.1342613],"genre_scores_gemma":[0.812759,0.001691281,0.08982672,0.001514117,0.0003012388,0.002144396,0.007613488,0.001058771,0.08309104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02275027,"threshold_uncertainty_score":0.0761072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660688877913006,"score_gpt":0.2572892256731453,"score_spread":0.2406823368940152,"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."}}