{"id":"W4399726181","doi":"10.1016/j.bios.2024.116499","title":"Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception","year":2024,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sensor fusion; Wearable computer; Electronics; Wearable technology; Data processing; Robotics; Artificial intelligence; Field (mathematics); Data acquisition; Embedded system; Human–computer interaction; Robot; Engineering; Electrical engineering; Database","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":[0.0009161552,0.0005770502,0.0004108777,0.0004819708,0.0001452618,0.0008865432,0.0009118536,0.000786209,0.002960984],"category_scores_gemma":[0.001165644,0.0003172238,0.0002892152,0.0008471338,0.0006187866,0.001711037,0.0005567595,0.000963534,0.0007675048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000445245,"about_ca_system_score_gemma":0.0003032294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003992319,"about_ca_topic_score_gemma":0.0006825985,"domain_scores_codex":[0.9997415,0.00003963256,0.00002056949,0.00008162008,0.00008968676,0.00002687846],"domain_scores_gemma":[0.9988902,0.0005889976,0.000137208,0.000080761,0.0002210747,0.00008175267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003374109,0.0002279385,0.001123659,0.003707825,0.0001158838,0.0001564466,0.0001525572,0.002964615,0.336824,0.01671468,0.006357224,0.6313179],"study_design_scores_gemma":[0.00004931087,0.001005229,0.003146019,0.0003658724,0.0002043608,0.001330283,0.0001701896,0.03966117,0.6562948,0.009300354,0.2883214,0.00015113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0635728,0.6983843,0.2084918,0.006287775,0.001034509,0.00009855899,0.0002810937,0.0006800882,0.02116901],"genre_scores_gemma":[0.4128678,0.3877159,0.1701842,0.003679587,0.002428506,0.0001755175,0.0005674274,0.0002772833,0.0221038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002960984,"threshold_uncertainty_score":0.009905457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02748482968300913,"score_gpt":0.2760186274058474,"score_spread":0.2485337977228383,"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."}}