{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003464384,0.00005538176,0.00005874928,0.00004728106,0.0001533501,0.00001956319,0.0001763334,0.00001731758,0.00001464439],"category_scores_gemma":[0.000001617309,0.00004784445,0.00001040493,0.00008781923,0.00002336007,0.00007553241,0.0008343509,0.00005075971,0.000009139585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003321368,"about_ca_system_score_gemma":4.484231e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002693491,"about_ca_topic_score_gemma":0.000003376538,"domain_scores_codex":[0.9993125,0.00001937798,0.0001254881,0.0002273083,0.000148445,0.0001668506],"domain_scores_gemma":[0.9997917,0.00001001382,0.00001782422,0.000166078,0.000001339349,0.00001308514],"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":[0.0001116097,0.0001803834,0.414432,0.0004640105,0.00003750201,0.00002703594,0.002758819,0.04297675,0.4728671,0.009397653,0.001450353,0.05529682],"study_design_scores_gemma":[0.0007400631,0.0001564707,0.03320483,0.00002051066,0.00001609903,0.000008392612,0.008412093,0.003000154,0.9168449,0.004982024,0.03228633,0.0003281231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960458,0.00001658212,0.002545284,0.00004459314,0.0001992808,0.0006195472,0.000001093278,0.000170038,0.0003578147],"genre_scores_gemma":[0.9893228,0.000002675244,0.008164009,0.000002207488,0.00001977044,0.00178924,0.000001358075,0.000007338508,0.0006906359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4439778,"threshold_uncertainty_score":0.195104,"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."}}