{"id":"W4393559316","doi":"10.5281/zenodo.7085409","title":"Synthetic population for Canada at the DA level for 2016, 2021, 2023 and 2030.","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"UK Research and Innovation","keywords":"Population; Geography; Environmental science; Environmental health; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001367296,0.0002345202,0.0002018848,0.00007265808,0.004547435,0.0002642951,0.0008802841,0.00007699049,0.1778414],"category_scores_gemma":[0.000966757,0.0002210727,0.00005058944,0.0002251758,0.0002093084,0.0001371994,0.002465532,0.000336754,0.001393197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273906,"about_ca_system_score_gemma":0.00001412695,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04590261,"about_ca_topic_score_gemma":0.009565442,"domain_scores_codex":[0.9973233,0.000407025,0.000317765,0.0008343747,0.000592734,0.0005247704],"domain_scores_gemma":[0.9986405,0.0002057284,0.0002309323,0.0006901505,0.00003929323,0.0001933347],"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":[0.00004055318,0.00003067954,0.000007092655,0.00007713212,0.00002151976,0.000003415811,0.00005281105,0.0001386722,0.00006293083,0.000009815869,0.9795476,0.0200078],"study_design_scores_gemma":[0.0003374477,0.0001242197,0.001056093,0.00001971541,0.00004465648,0.00002788268,0.0001222166,0.0001776951,0.00001444045,0.00004886472,0.997779,0.0002478387],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001047619,0.00009066018,0.0004174431,0.001601402,0.0002050081,0.002221927,0.9929765,0.00004359215,0.001395863],"genre_scores_gemma":[0.001360659,0.0003318176,0.00007263055,0.0006827461,0.0001235859,0.000002745454,0.9921278,0.0007779257,0.004520081],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1764482,"threshold_uncertainty_score":0.9993843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04043178990754417,"score_gpt":0.2509078168162907,"score_spread":0.2104760269087465,"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."}}