{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713883,0.001995454,0.001133615,0.002422664,0.0007079766,0.001973423,0.002061526,0.0009498599,0.08573295],"category_scores_gemma":[0.00585531,0.0009692794,0.002037634,0.002407947,0.0003935435,0.001311878,0.002300555,0.001919908,0.1009822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007704855,"about_ca_system_score_gemma":0.001828901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008040885,"about_ca_topic_score_gemma":0.01671516,"domain_scores_codex":[0.9989671,0.0001493054,0.0001560353,0.0003434969,0.0002519785,0.0001321151],"domain_scores_gemma":[0.9986137,0.0003866392,0.000106234,0.0004010355,0.0003763768,0.0001160305],"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":[0.0000972417,0.00002598765,0.001261977,0.0005189407,0.00006593334,0.00004190422,0.00005742006,0.0009027295,0.000840818,0.0008561054,0.9875902,0.007740745],"study_design_scores_gemma":[0.0002479145,0.00003768562,0.005407614,0.0001991316,0.00006225467,0.0001318887,0.00009226081,0.007722527,0.003689393,0.007811808,0.9744927,0.0001048147],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0005966188,0.00006758094,0.006646528,0.0001091427,0.00009819934,0.0001030729,0.9527571,0.03790889,0.001712745],"genre_scores_gemma":[0.002384311,0.00007038693,0.01193311,0.0001949434,0.00002867353,0.000552092,0.9732843,0.009667701,0.001884443],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.08573295,"threshold_uncertainty_score":0.2868053,"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."}}