{"id":"W4396655929","doi":"10.24908/agt.v2i1.17204","title":"Optimize Performance Using A Hydration Biosensor","year":2024,"lang":"en","type":"article","venue":"Aging and (Geron) Technology","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Biosensor; Recreation; Athletes; Cycling; Galvanic cell; Competitive athletes; Computer science; Nanotechnology; Materials science; Physical therapy; Medicine; Ecology; Biology","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":[0.0001130361,0.0001117545,0.0001693161,0.0003953224,0.0001103457,0.00002579705,0.00003533247,0.0001340097,0.00001540234],"category_scores_gemma":[0.000006659741,0.00009497259,0.00002661945,0.0003542298,0.0001187655,0.0001357568,0.00003045603,0.0002560776,0.00002407089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886788,"about_ca_system_score_gemma":0.00005112987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001035387,"about_ca_topic_score_gemma":0.000001569464,"domain_scores_codex":[0.9993122,0.000001928395,0.0001508758,0.0002401988,0.00006737166,0.0002274487],"domain_scores_gemma":[0.9997514,0.000009587723,0.00002358744,0.0001578134,0.00002159985,0.0000359838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001156773,0.0001232158,0.2362778,0.00173442,0.0003443713,0.0006242089,0.003025794,0.000444564,0.05173063,0.003949293,0.0002863054,0.7013437],"study_design_scores_gemma":[0.003251685,0.001189775,0.03977001,0.006517991,0.0008546835,0.00756165,0.002831957,0.8018013,0.09069064,0.0004546695,0.04403364,0.001042039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930798,0.003380783,0.000260758,0.001378365,0.0001899902,0.00009811013,5.20548e-7,0.0005481718,0.001063497],"genre_scores_gemma":[0.9952059,0.000487231,0.003679854,0.0001380523,0.0001169993,0.000007949591,0.000005061919,0.00001973139,0.0003392416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8013567,"threshold_uncertainty_score":0.387287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02161059895383678,"score_gpt":0.2854965868095081,"score_spread":0.2638859878556714,"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."}}