{"id":"W7102415238","doi":"10.6084/m9.figshare.30475016.v7","title":"Additional file of Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020","year":2025,"lang":"","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Queen's University","funders":"","keywords":"Big data; Open data; Equity (law); Open source; Real world data; Data collection","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":["open_science"],"domain":null,"study_design":"observational","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science","insufficient_payload"],"category_scores_codex":[0.000735399,0.001339805,0.002340817,0.0008597103,0.0004955964,0.003032677,0.01648073,0.001244244,0.9985393],"category_scores_gemma":[0.009087799,0.001515241,0.0001713793,0.002424349,0.0002004629,0.003295864,0.04943261,0.001050909,0.001324589],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009083053,"about_ca_system_score_gemma":0.007071874,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01626904,"about_ca_topic_score_gemma":0.03241739,"domain_scores_codex":[0.9888334,0.0007667458,0.002265737,0.005672072,0.001470223,0.0009917727],"domain_scores_gemma":[0.9836928,0.005369249,0.002789366,0.006879444,0.0007544805,0.0005146827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000569311,0.0005304967,0.001963747,0.002082592,0.001091728,0.00003234844,0.00005749683,0.0001033077,0.000495132,1.162132e-7,0.9881151,0.004958638],"study_design_scores_gemma":[0.001854555,0.00007657441,0.4447418,0.02359273,0.0001873114,0.000009178674,0.0001188548,0.08383757,0.00006127422,0.00003734342,0.4440672,0.00141555],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000194343,0.001669963,0.00000882744,0.0000121285,0.0001919438,0.002888734,0.9902888,0.00005630105,0.004688936],"genre_scores_gemma":[0.000606399,0.00001265724,0.007323301,0.0002306549,0.00123606,0.0006275894,0.9833667,0.0004723245,0.006124336],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9972147,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.334455149662149,"score_gpt":0.4626660608633364,"score_spread":0.1282109112011874,"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."}}