{"id":"W7084438019","doi":"10.6084/m9.figshare.30262101","title":"Additional file 3 of Predicting Future Performance in Powerlifting: A Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Training set; Task (project management); Feature (linguistics); Support vector machine; Key (lock)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001136198,0.001220942,0.001064538,0.002003319,0.0005795651,0.001629994,0.00212536,0.001937195,0.9011313],"category_scores_gemma":[0.0269444,0.0005451246,0.001341918,0.002168847,0.0002833391,0.001483893,0.0009165193,0.001021368,0.3191381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007677367,"about_ca_system_score_gemma":0.001282707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009550095,"about_ca_topic_score_gemma":0.02146194,"domain_scores_codex":[0.9994808,0.00009574492,0.00006546326,0.0001620915,0.0001199564,0.00007596081],"domain_scores_gemma":[0.9779487,0.01811855,0.0005861595,0.001194325,0.001817304,0.0003350274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003116252,0.0001766583,0.003064157,0.001605082,0.00006381673,0.00007102459,0.00004047515,0.001455254,0.00007427848,0.0006026416,0.9770169,0.01551813],"study_design_scores_gemma":[0.008387857,0.0009389028,0.07023878,0.003567808,0.0005470456,0.0008340793,0.0007978067,0.02536332,0.002422298,0.0356822,0.8507724,0.0004475586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003759278,0.00002063895,0.0005261279,0.00007878913,0.00004204684,0.00005087615,0.9968443,0.0006470006,0.00141448],"genre_scores_gemma":[0.02047636,0.0001545418,0.009764202,0.0006065836,0.0001703806,0.001805503,0.9403661,0.001819761,0.02483664],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9011313,"threshold_uncertainty_score":0.1410242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571472939282999,"score_gpt":0.2011876661403348,"score_spread":0.1854729367475048,"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."}}