{"id":"W4401589962","doi":"10.20944/preprints202408.0489.v1","title":"A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Cycling; Computer science; Artificial intelligence; Machine learning; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005524952,0.0007293997,0.0005672968,0.0008259101,0.0003134192,0.0005114904,0.0005259126,0.0008276575,0.000782531],"category_scores_gemma":[0.001933177,0.0003392004,0.0005178099,0.0005851857,0.0001812068,0.0003358001,0.0003231406,0.0006620605,0.0002890887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003699596,"about_ca_system_score_gemma":0.0005856781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173191,"about_ca_topic_score_gemma":0.008043095,"domain_scores_codex":[0.9997978,0.00003517913,0.00002398748,0.00007811302,0.00004214315,0.00002285915],"domain_scores_gemma":[0.9995887,0.000227212,0.00003972124,0.00002278394,0.0001057527,0.00001600158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009666985,0.0001420685,0.005173947,0.00006421783,0.00006868137,0.00008755943,0.00004476561,0.7838709,0.003146277,0.0004272573,0.0006885834,0.2061891],"study_design_scores_gemma":[0.000001437929,0.00001942222,0.0007082312,0.000004297352,0.000003732287,0.000007046282,0.000003963223,0.9987991,0.0001798186,0.0001867066,0.00008328645,0.000002922097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175282,0.00159095,0.8187955,0.000299452,0.0001021634,0.0001399021,0.0003569277,0.00107606,0.002357082],"genre_scores_gemma":[0.9222445,0.0004887201,0.07401866,0.0001032544,0.00005352854,0.0001969649,0.000437962,0.00003025606,0.002426117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01173191,"threshold_uncertainty_score":0.02332729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.140727556293411,"score_gpt":0.3141099492468932,"score_spread":0.1733823929534822,"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."}}