{"id":"W4399583564","doi":"10.32614/cran.package.hmdhfdplus","title":"HMDHFDplus: Read Human Mortality Database and Human Fertility Database Data from the Web","year":2015,"lang":"en","type":"dataset","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Database; Computer science; Human fertility; Fertility; Medicine; Population; Environmental health","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":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01700696,0.0006284436,0.0009032546,0.0002600319,0.001099912,0.001333704,0.007154181,0.000355711,0.00414791],"category_scores_gemma":[0.00424224,0.000373628,0.0001214114,0.0007031343,0.0007644367,0.001380156,0.005129698,0.0009534976,0.0005698426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000375242,"about_ca_system_score_gemma":0.0003034619,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05952705,"about_ca_topic_score_gemma":0.164998,"domain_scores_codex":[0.989839,0.001194237,0.001835036,0.002837702,0.003778516,0.0005155036],"domain_scores_gemma":[0.9799985,0.001793112,0.0008735856,0.01633157,0.0005559406,0.0004472845],"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":[0.00002793047,0.0001506573,0.0014805,0.00001813101,0.00007861917,0.00001981173,0.00002791359,0.000001663149,0.00003844983,0.00007524225,0.9977157,0.000365451],"study_design_scores_gemma":[0.0005765932,0.00004030437,0.006831634,0.00009196978,0.0003927668,0.000003214932,0.0006945259,0.002685951,0.000001398864,0.007876593,0.9802665,0.0005385186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02186925,0.0005950703,0.0003104889,0.0003276879,0.0005217207,0.0004960179,0.9750001,0.00009000814,0.0007896273],"genre_scores_gemma":[0.006001103,0.0002993343,0.0002884796,0.000697394,0.0003190055,0.00002201492,0.9918757,0.00002496518,0.0004720235],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.105471,"threshold_uncertainty_score":0.9998716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4882797242371081,"score_gpt":0.4840184770172373,"score_spread":0.004261247219870812,"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."}}