{"id":"W4409993320","doi":"10.1186/isrctn95727228","title":"High versus low load training in females","year":2025,"lang":"en","type":"dataset","venue":"http://isrctn.com/","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Training (meteorology); Biology; Computer science; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057503,0.0007505958,0.001484414,0.0006884659,0.0001472115,0.0000681789,0.000481962,0.0007911253,0.000803126],"category_scores_gemma":[0.0002130432,0.0007118057,0.0003121949,0.0007126895,0.0001752509,0.0001847207,0.0001882667,0.001698369,0.0002116265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004468943,"about_ca_system_score_gemma":0.001752037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100173,"about_ca_topic_score_gemma":0.001035399,"domain_scores_codex":[0.9963103,0.00002870061,0.0009334431,0.0009196351,0.0008381685,0.0009697735],"domain_scores_gemma":[0.9978641,0.0001777109,0.000311068,0.001262109,0.0001280729,0.0002568964],"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":[0.001103441,0.0002198346,0.00158486,0.001175147,0.0002849858,0.001581998,0.0006119917,0.00003123169,0.000006848787,0.000122141,0.983644,0.00963353],"study_design_scores_gemma":[0.007111785,0.0004354389,0.005821088,0.003615839,0.0004808134,0.00006328784,0.0009724129,0.0001026154,0.00003936267,0.0000264046,0.9806104,0.0007205049],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07489613,0.002479279,0.000007044282,0.001964987,0.009124893,0.001669702,0.9027628,0.0003941062,0.006701032],"genre_scores_gemma":[0.03033355,0.001092547,0.0002301415,0.001161778,0.001208741,0.0001188466,0.9633303,0.00005465464,0.002469457],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06056746,"threshold_uncertainty_score":0.9995333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04626097981361817,"score_gpt":0.315932906711769,"score_spread":0.2696719268981509,"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."}}