{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006461416,0.0009103497,0.0004909956,0.0004275409,0.0003163528,0.0009872214,0.0008949297,0.0009672698,0.003405333],"category_scores_gemma":[0.001403057,0.0002601577,0.0003693353,0.0002637722,0.0002492781,0.0008202173,0.0008710577,0.0004555241,0.002088202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287521,"about_ca_system_score_gemma":0.0004784977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008909912,"about_ca_topic_score_gemma":0.001277658,"domain_scores_codex":[0.9994129,0.00007279894,0.00003542639,0.0001258502,0.0002663538,0.00008674085],"domain_scores_gemma":[0.9996371,0.00006613639,0.00004884039,0.0000296146,0.0001604767,0.0000577881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002841749,0.00051537,0.003548656,0.0002710028,0.00003033188,0.0001025838,0.0001743383,0.001666092,0.8875624,0.0007505455,0.003079112,0.1020152],"study_design_scores_gemma":[0.00006694198,0.002693535,0.006948869,0.00007087406,0.00008123914,0.0002700405,0.0001787995,0.01904454,0.9423692,0.0007284964,0.02747033,0.00007717784],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6868043,0.004245838,0.2670434,0.00330993,0.0009105643,0.0009122244,0.0007978263,0.004177589,0.03179827],"genre_scores_gemma":[0.844643,0.002175392,0.1224277,0.001354882,0.0002186347,0.0005035214,0.000541002,0.0003805581,0.02775542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003405333,"threshold_uncertainty_score":0.011392,"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."}}