{"id":"W6930397236","doi":"10.5281/zenodo.11521745","title":"SIDRRpy v1.0: SIDRR dataset analysis code","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Environment and Climate Change Canada","funders":"","keywords":"Python (programming language); Code (set theory); Process (computing); Source code; Deformation monitoring; Toolbox","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001566044,0.0002999498,0.0003927147,0.0015173,0.001908786,0.001769634,0.002110012,0.0002307603,0.1066148],"category_scores_gemma":[0.000325047,0.0003270044,0.0002212943,0.002946653,0.0005967692,0.0001682814,0.001388317,0.0004649232,0.0636225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002091658,"about_ca_system_score_gemma":0.000009678394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001615775,"about_ca_topic_score_gemma":0.0004092963,"domain_scores_codex":[0.996101,0.0008405957,0.0003558833,0.0009215198,0.00113563,0.0006453606],"domain_scores_gemma":[0.9981648,0.00002090263,0.000235627,0.001072683,0.0002360418,0.000269978],"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":[0.00000829623,0.000083284,0.00001761361,0.0001228047,0.0009433964,0.00004805125,0.001018924,0.000009790139,0.000002819168,0.01243746,0.9772326,0.008074911],"study_design_scores_gemma":[0.0001434054,0.00003666517,0.0002504715,0.00005370589,0.0005154778,0.000002139114,0.0007138831,0.00003219202,0.000001327332,0.0003145402,0.9975837,0.0003524479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002621446,0.0006536915,0.0005774974,0.0007668893,0.0003791512,0.0007394248,0.01461899,0.001950195,0.980288],"genre_scores_gemma":[0.01882228,0.003666614,0.0004172476,0.0005785641,0.001757739,3.676386e-7,0.05974887,0.01527221,0.8997361],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08055184,"threshold_uncertainty_score":0.9999182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04307340391146578,"score_gpt":0.3164744892103995,"score_spread":0.2734010852989337,"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."}}