{"id":"W6921169987","doi":"10.6084/m9.figshare.22617022.v1","title":"Additional file 1 of Building capacity in quantitative research and data storytelling to enhance knowledge translation: a training curriculum for peer researchers","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Toronto","funders":"","keywords":"Curriculum; Training (meteorology); Storytelling; Research data; Quantitative analysis (chemistry); Peer assessment","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["insufficient_payload"],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01296256,0.001411329,0.001545022,0.003846304,0.001748337,0.002808164,0.002533143,0.002015671,0.9365327],"category_scores_gemma":[0.1611412,0.00140927,0.0009771149,0.004546779,0.0007317645,0.003591624,0.003173787,0.002029154,0.4643238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002042772,"about_ca_system_score_gemma":0.00677208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00345064,"about_ca_topic_score_gemma":0.009199929,"domain_scores_codex":[0.9943902,0.002527065,0.0009033829,0.0005757308,0.001165596,0.0004380595],"domain_scores_gemma":[0.7405651,0.2179125,0.005093697,0.01161656,0.02160159,0.003210503],"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":[0.00009523144,0.0000946851,0.0002762509,0.0008470124,0.000005445452,0.00001702867,0.00009917538,0.0001239915,0.00002237245,0.0006412304,0.9863513,0.0114264],"study_design_scores_gemma":[0.005034058,0.0002425962,0.005608653,0.002983059,0.00006153668,0.000143236,0.001041631,0.001784391,0.0007485978,0.02192756,0.9602737,0.0001510725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004805174,0.00002763661,0.004496183,0.001266786,0.0002502785,0.00350982,0.9715962,0.003163055,0.01520966],"genre_scores_gemma":[0.01743438,0.0002602241,0.06813256,0.003796831,0.0005610301,0.1020958,0.7003437,0.009721472,0.09765407],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9365327,"threshold_uncertainty_score":0.09052825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9409248530033029,"score_gpt":0.6367714792365394,"score_spread":0.3041533737667634,"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."}}