{"id":"W4394215213","doi":"10.6084/m9.figshare.22581730","title":"Material stock and population 1978-2020","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency","funders":"","keywords":"Stock (firearms); Population; Geography; Demography; Archaeology; Sociology","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006755575,0.0002188585,0.0005446698,0.0002254924,0.00009143462,0.000212906,0.000242328,0.0002830271,0.0728332],"category_scores_gemma":[0.0002178562,0.0002621961,0.0001463976,0.0001419411,0.000005129917,0.0001055236,0.0001953742,0.0001520292,0.03065459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006455163,"about_ca_system_score_gemma":0.00001420609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003741375,"about_ca_topic_score_gemma":0.0008425256,"domain_scores_codex":[0.9987199,0.000007570927,0.0005256147,0.0005147545,0.00003168584,0.000200531],"domain_scores_gemma":[0.9990327,0.00004023413,0.0004899278,0.000331945,0.00001840251,0.00008673255],"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":[0.00000407485,0.000007937187,0.0000425267,0.0001321278,0.00005921728,0.000007741952,0.000002600687,0.00001616141,2.740748e-8,0.0001501544,0.9994869,0.0000905256],"study_design_scores_gemma":[0.00009744742,0.00002033417,0.00438152,0.0001330723,0.00001024542,0.000002262511,0.000001286249,0.0004514632,1.911713e-7,0.001696751,0.9929022,0.0003032514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009271065,0.00024803,1.213951e-7,0.0002117384,0.0003084746,0.0001492626,0.9987943,0.00002903684,0.0001662847],"genre_scores_gemma":[0.0001900845,0.0002092221,0.000007456968,0.0001432849,0.0006217799,0.0001073641,0.9982975,0.00002537472,0.0003979238],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0421786,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05427595438314849,"score_gpt":0.2326418903718278,"score_spread":0.1783659359886793,"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."}}