{"id":"W4409255266","doi":"10.1016/j.jsampl.2025.100097","title":"Don’t go chasing waterfalls: Multiple factor prediction of injuries in a performance context","year":2025,"lang":"en","type":"article","venue":"JSAMS Plus","topic":"Sports injuries and prevention","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Circus School","funders":"","keywords":"Context (archaeology); Factor (programming language); Psychology; Computer science; Geography; Archaeology","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":[0.001017983,0.0004662063,0.000330177,0.000925369,0.0006210104,0.001092333,0.0004054919,0.0004915285,0.002630594],"category_scores_gemma":[0.003318328,0.0002330565,0.0005511671,0.0006796475,0.0004027729,0.0005351568,0.000883758,0.0009384951,0.0003517939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003088147,"about_ca_system_score_gemma":0.0007470997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01580591,"about_ca_topic_score_gemma":0.04189124,"domain_scores_codex":[0.9995442,0.0001300237,0.00003598238,0.00006789445,0.0001035074,0.0001183228],"domain_scores_gemma":[0.9983948,0.0002909943,0.0006662343,0.00008262534,0.000171211,0.0003940128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002174064,0.0000445887,0.9971385,0.000005401511,0.00002551663,0.0000345336,0.0000717021,0.000086319,0.00006470805,0.00001989937,0.00009711827,0.002389965],"study_design_scores_gemma":[0.000001681739,0.00008581745,0.9972637,0.00002163146,0.00002424895,0.00009517761,0.0006689735,0.001489921,0.00005523517,0.00008963297,0.0001996153,0.000004347584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987038,0.00008732391,0.000289742,0.000253829,0.00001724053,0.000008313958,0.00007692099,0.000003251015,0.000559439],"genre_scores_gemma":[0.999333,0.00006435499,0.0002313498,0.00001795182,0.00001420039,0.000003617488,0.00007556254,0.000001814294,0.0002582747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01580591,"threshold_uncertainty_score":0.0314278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779379992305393,"score_gpt":0.2698345947376098,"score_spread":0.2520407948145559,"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."}}