{"id":"W1987215386","doi":"10.1159/000314281","title":"Demographic Changes in Germany up to 2060 – Consequences for Blood Donation","year":2010,"lang":"en","type":"article","venue":"Transfusion Medicine and Hemotherapy","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Life expectancy; Demography; Population; Age structure; Demographic change; Fertility; Emigration; Projections of population growth; Population projection; Population ageing; Donation; Quarter (Canadian coin); Balance (ability); Immigration; Gerontology; Geography; Medicine; Economics; Economic growth; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008588161,0.0000981416,0.0001603843,0.0003074282,0.0002702535,0.00002131329,0.00008482471,0.0001077643,0.0003957609],"category_scores_gemma":[0.00007850997,0.00008353833,0.00002283411,0.0004009834,0.0002674286,0.0001021896,0.000001980505,0.00009303394,0.000001385466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001090675,"about_ca_system_score_gemma":0.00004815547,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005212528,"about_ca_topic_score_gemma":0.4364456,"domain_scores_codex":[0.9991114,0.00005299723,0.0001772596,0.0002142968,0.0002465824,0.0001975155],"domain_scores_gemma":[0.9994488,0.0002109238,0.00004474213,0.00007717274,0.0001116504,0.0001067344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001850409,0.00008059401,0.01540111,0.00006247714,0.0000171512,0.000002169914,0.2709652,0.000001150033,0.6107171,0.02103837,0.001375294,0.08015436],"study_design_scores_gemma":[0.02091682,0.003129095,0.160982,0.0007858997,0.0002330099,0.00002330039,0.1245866,0.000726402,0.02414064,0.04213025,0.620804,0.001541888],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9178758,0.0002194743,0.0007930539,0.07920798,0.0004156605,0.0008509798,0.000006731582,0.00004535832,0.0005849663],"genre_scores_gemma":[0.9741773,0.01914452,0.0009393175,0.004303755,0.0006783337,0.000146079,0.00001977591,0.00001780637,0.0005731315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6194287,"threshold_uncertainty_score":0.7879817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08089888019260465,"score_gpt":0.3510382958689615,"score_spread":0.2701394156763568,"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."}}