{"id":"W2108732404","doi":"10.1504/ijhtm.2009.023726","title":"Ubiquitous and personalised contextual biomonitoring","year":2009,"lang":"en","type":"article","venue":"International Journal of Healthcare Technology and Management","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Biomonitoring; Context (archaeology); Computer science; Adaptation (eye); Quality (philosophy); Inference; Data science; Geography; Ecology; Artificial intelligence; Psychology","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":[0.0002984504,0.00008915389,0.0001594958,0.0007548172,0.00006670324,0.0000843224,0.0004705369,0.00007212772,0.000001681164],"category_scores_gemma":[0.00002422093,0.00008253348,0.00003258552,0.0001731971,0.00005131373,0.0003467269,0.0001581623,0.0001715496,0.000002034304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006754659,"about_ca_system_score_gemma":0.00002347389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001528054,"about_ca_topic_score_gemma":0.000007668712,"domain_scores_codex":[0.9990913,0.00003575183,0.0002985276,0.0001688052,0.0002724042,0.0001331789],"domain_scores_gemma":[0.9992592,0.00003871277,0.0002261815,0.0001132067,0.0002963674,0.00006631216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002888656,0.00004385103,0.001619665,0.00001612506,0.0001008713,0.0003144337,0.0002487773,2.895725e-7,0.0002454371,0.1387444,0.0001726762,0.8584646],"study_design_scores_gemma":[0.02040773,0.00735426,0.2149224,0.004093957,0.0002081548,0.03084613,0.01433948,0.003450755,0.01216606,0.3539588,0.3358931,0.002359201],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.532059,0.006341372,0.158438,0.2980774,0.003349229,0.0004356658,0.000006314702,0.0002671615,0.001025859],"genre_scores_gemma":[0.9950715,0.0006863881,0.003253605,0.000819494,0.0001144276,0.000003239692,3.402465e-7,0.000002742477,0.00004828893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8561054,"threshold_uncertainty_score":0.3365618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778563733828033,"score_gpt":0.3071358654828529,"score_spread":0.2893502281445726,"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."}}