{"id":"W2997283575","doi":"10.1109/iotsms48152.2019.8939234","title":"Indoor Occupancy Prediction using an IoT Platform","year":2019,"lang":"en","type":"article","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Occupancy; Software portability; Computer science; Cloud computing; Building automation; Wireless sensor network; Scalability; Real-time computing; Home automation; Internet of Things; Embedded system; Database; Computer network; Telecommunications; Engineering; Operating system; Architectural engineering","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":[0.0001589936,0.0005615507,0.0004326971,0.0004365019,0.0002514528,0.0005173893,0.0005490264,0.0003561054,0.001139089],"category_scores_gemma":[0.0005794775,0.0002148531,0.000303293,0.0004266489,0.0001085851,0.0005574084,0.0005601646,0.0002770876,0.00050035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552694,"about_ca_system_score_gemma":0.000362098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00407989,"about_ca_topic_score_gemma":0.004834681,"domain_scores_codex":[0.9998252,0.00001807626,0.00001072336,0.00005009469,0.00006780992,0.00002809297],"domain_scores_gemma":[0.9998243,0.00004066759,0.00002857047,0.00002744212,0.00005663328,0.00002230258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007659764,0.000591356,0.04554307,0.0002123946,0.0001248141,0.0007102714,0.0001540183,0.6413351,0.0386808,0.003423903,0.006065565,0.2623927],"study_design_scores_gemma":[0.000004869335,0.00003511141,0.00251003,0.000004148148,0.00000716979,0.00003148528,0.00001530236,0.9937562,0.002660578,0.0004017763,0.0005669624,0.000006429435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4710104,0.0002306784,0.5118746,0.0001816351,0.0001935777,0.0002064405,0.001260722,0.006798466,0.008243493],"genre_scores_gemma":[0.9287983,0.00008774751,0.06828907,0.00003224648,0.00003209675,0.00007612419,0.0009800398,0.0000469094,0.001657474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00407989,"threshold_uncertainty_score":0.008112252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05520905697150198,"score_gpt":0.2786217849002076,"score_spread":0.2234127279287056,"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."}}