{"id":"W4286984751","doi":"10.48550/arxiv.2109.08672","title":"Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case\\n Study","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Activities of daily living; Computer science; Assisted living; Identification (biology); Internet of Things; Real-time computing; Dependency (UML); Field (mathematics); Artificial intelligence; Human–computer interaction; Computer vision; Internet privacy; Psychology; Gerontology; Mathematics; Ecology; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00293243,0.0008804532,0.001355819,0.0006132458,0.0007237854,0.0005758816,0.002308317,0.0004609434,0.00003828149],"category_scores_gemma":[0.0002972826,0.001129371,0.0006711291,0.002204756,0.0003009521,0.00187058,0.006131548,0.002169171,0.000005446982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000501848,"about_ca_system_score_gemma":0.001033539,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006631864,"about_ca_topic_score_gemma":0.0001745487,"domain_scores_codex":[0.9921564,0.002971525,0.0006954792,0.002839563,0.0003652154,0.0009717558],"domain_scores_gemma":[0.9932734,0.0013248,0.001279251,0.002954226,0.0008312928,0.0003369706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005366995,0.001354601,0.8121368,0.0002154343,0.0005649684,0.03406342,0.008875852,0.120774,0.00367476,0.00007964674,2.509692e-7,0.01820661],"study_design_scores_gemma":[0.001236977,0.0002059355,0.2839469,0.001445928,0.0005709956,0.0009733089,0.01755086,0.6871392,0.005295398,0.0001225518,0.000002499769,0.001509444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6873453,0.0001955021,0.3097626,0.000006822377,0.001941838,0.0004784241,0.000004671519,0.00009860285,0.0001662715],"genre_scores_gemma":[0.9934215,0.00009503825,0.006047397,0.000009733454,0.0002911868,0.000001234731,5.004999e-7,0.00006220555,0.00007123345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5663652,"threshold_uncertainty_score":0.9999831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1600623511207876,"score_gpt":0.2657904172904256,"score_spread":0.1057280661696379,"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."}}