{"id":"W4295722048","doi":"10.5281/zenodo.7078560","title":"R code for Demographic consequences of changing environmental periodicity","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Seventh Framework Programme; Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Code (set theory); Geography; Computer science; Programming language","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002136526,0.00009971088,0.0001433213,0.0003884776,0.007340365,0.0002124174,0.0009280037,0.00003049965,0.00769675],"category_scores_gemma":[0.0001753186,0.0001204066,0.0001069721,0.0007400864,0.0009354292,0.0001593727,0.0007694418,0.000158954,0.0001140361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001571905,"about_ca_system_score_gemma":0.000006860618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009625167,"about_ca_topic_score_gemma":0.000006568383,"domain_scores_codex":[0.9977452,0.0006146361,0.0002381901,0.0003212596,0.0006556268,0.0004250605],"domain_scores_gemma":[0.9993498,0.00003541879,0.0001656697,0.000244473,0.000104809,0.00009977172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007089914,0.002253221,0.0127145,0.0005545513,0.0007959423,0.00006424094,0.1342234,0.001346287,0.02686587,0.4644199,0.1309776,0.2250755],"study_design_scores_gemma":[0.0003453713,0.0001931767,0.003679781,0.000007249532,0.00002303498,0.000006211421,0.01680326,0.0001062891,0.000169776,0.000829123,0.9776832,0.0001534753],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9025468,0.0004066452,0.002850436,0.002281934,0.0004523354,0.002438902,0.002907506,0.0007689408,0.08534649],"genre_scores_gemma":[0.9983898,0.0001594078,0.0001753256,0.0001323793,0.00007202852,5.489059e-7,0.0003860143,0.0002836707,0.0004007699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8467057,"threshold_uncertainty_score":0.9939519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03609661189114434,"score_gpt":0.2666703678245051,"score_spread":0.2305737559333608,"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."}}