{"id":"W3170222427","doi":"10.5194/agile-giss-2-9-2021","title":"Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors","year":2021,"lang":"en","type":"article","venue":"AGILE GIScience Series","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Cisco Systems","keywords":"Occupancy; Event (particle physics); Computer science; Gyroscope; Workflow; Real-time computing; Accelerometer; Task (project management); Internet of Things; Simulation; Database; Engineering; Embedded system; Systems engineering; Aerospace engineering; Architectural engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009792869,0.0009224794,0.0008210451,0.0007385975,0.0005044941,0.001104247,0.001416039,0.00105234,0.002459452],"category_scores_gemma":[0.00280551,0.0004908125,0.0008903281,0.000597374,0.0004093657,0.0008136205,0.0007224269,0.0009856997,0.0004280154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009024256,"about_ca_system_score_gemma":0.001242014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02130508,"about_ca_topic_score_gemma":0.01757797,"domain_scores_codex":[0.9995951,0.00009062025,0.00002842495,0.00009847632,0.0001129391,0.0000743929],"domain_scores_gemma":[0.9988381,0.0007063947,0.0001175662,0.00005188084,0.0002343336,0.00005181423],"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":[0.00004105839,0.00001680328,0.001102546,0.00003311964,0.00001526449,0.00005681613,0.00003256555,0.9897418,0.0003818628,0.00192306,0.0002676776,0.006387407],"study_design_scores_gemma":[0.000001142683,0.000006825896,0.00007148687,0.000002366786,0.000002345444,0.000005543971,0.000005237845,0.999223,0.00007378959,0.0005185714,0.00008783027,0.00000194641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1049842,0.0004710346,0.88433,0.0006669614,0.0001617132,0.0001504732,0.0007886461,0.00101947,0.007427536],"genre_scores_gemma":[0.9533476,0.0002318302,0.0413403,0.00009460745,0.00004490754,0.0001767669,0.0005235709,0.00005164919,0.004188699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02130508,"threshold_uncertainty_score":0.04236215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03068985332120091,"score_gpt":0.2399161141575706,"score_spread":0.2092262608363697,"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."}}