{"id":"W4379932127","doi":"10.1145/3587819.3592553","title":"A Dataset of Food Intake Activities Using Sensors with Heterogeneous Privacy Sensitivity Levels","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Activity recognition; Computer science; Sensitivity (control systems); Artificial intelligence; RGB color model; Machine learning; 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":[0.0003404474,0.0008363628,0.0007389924,0.001014518,0.0003192626,0.000427937,0.0008085542,0.0009172925,0.001275842],"category_scores_gemma":[0.001385072,0.0001632051,0.0007059133,0.001919469,0.0003093152,0.0004423308,0.0006466734,0.0007147493,0.0008910859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000326321,"about_ca_system_score_gemma":0.0004535046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005048747,"about_ca_topic_score_gemma":0.01317886,"domain_scores_codex":[0.999406,0.00009187905,0.00007879267,0.000176906,0.0001808675,0.00006565324],"domain_scores_gemma":[0.9991467,0.0002579934,0.0001208866,0.0001733458,0.0002194403,0.00008163823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003134122,0.002736559,0.3988378,0.007153171,0.001354827,0.004175429,0.001451575,0.04080212,0.06694093,0.002904958,0.1454013,0.3251074],"study_design_scores_gemma":[0.0002191453,0.001358819,0.7791595,0.0004914427,0.0003299815,0.003350342,0.002019474,0.05441617,0.02457873,0.003138502,0.1306985,0.0002394383],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6420876,0.00236765,0.0287758,0.00111419,0.0003462469,0.0006183643,0.3142002,0.002014246,0.008475697],"genre_scores_gemma":[0.5876142,0.001163156,0.04645832,0.0004954532,0.0001077759,0.0008325101,0.360125,0.00009554059,0.003108021],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.005048747,"threshold_uncertainty_score":0.01003873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001014141786982,"score_gpt":0.2927578384982077,"score_spread":0.1926564243195095,"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."}}