{"id":"W2607058759","doi":"10.1109/sas.2017.7894057","title":"Sensor modality shifting in IoT deployment: Measuring non-temperature data using temperature sensors","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Flexibility (engineering); Software deployment; Computer science; Modality (human–computer interaction); Real-time computing; Internet of Things; Temperature measurement; Wireless sensor network; Intelligent sensor; Building automation; Wearable computer; Measure (data warehouse); Doors; Embedded system; Artificial intelligence; Computer network; Data mining","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001678197,0.000363857,0.0004989697,0.0002025232,0.0008715991,0.002151405,0.003085047,0.0002627019,0.00001614291],"category_scores_gemma":[0.00047964,0.0003417014,0.00008828016,0.0002963513,0.00006793626,0.002688224,0.002101467,0.0006575367,0.00004454358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001763923,"about_ca_system_score_gemma":0.0001897809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001778512,"about_ca_topic_score_gemma":0.001253499,"domain_scores_codex":[0.996627,0.0002951726,0.0005375117,0.001299511,0.0006361317,0.0006046167],"domain_scores_gemma":[0.9948782,0.0001745642,0.0003544559,0.004197277,0.000206975,0.0001885562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008189894,0.0006006364,0.1505775,0.0004712459,0.0003208806,0.001124398,0.005407363,0.001698679,0.8030596,0.0008452206,0.0007647144,0.0350478],"study_design_scores_gemma":[0.004152657,0.00006933228,0.1328741,0.001837415,0.0000561534,0.0007006861,0.001095904,0.7171975,0.1376952,0.0004258013,0.001112867,0.002782366],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896048,0.0000520386,0.005939651,0.001553351,0.000794993,0.0004898204,0.00003667301,0.0002567612,0.001271852],"genre_scores_gemma":[0.9881835,0.000003864404,0.01097221,0.0002052223,0.0002921512,0.000008508346,0.000009773074,0.00003325113,0.0002915082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7154988,"threshold_uncertainty_score":0.9999035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1322246609935732,"score_gpt":0.3240246653231425,"score_spread":0.1918000043295693,"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."}}