{"id":"W2183374111","doi":"10.5281/zenodo.20716226","title":"USL-5UEs-UGNSE 0.25, 0.5, 1.0 for the policy makers & shapers: the USL- 5UE´s premises and their socio-economic and environmental snow ball effects for the governments and the United Nations (Draft 1)","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hyperion Technologies (Canada)","funders":"","keywords":"Premises; Ball (mathematics); Snow; Business; Political science; Geography; Mathematics; Meteorology; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02376123,0.001349066,0.00107808,0.004402267,0.003303238,0.01204198,0.003709938,0.01168325,0.1337594],"category_scores_gemma":[0.05091691,0.001934692,0.001900463,0.006005215,0.002348255,0.00679339,0.006463964,0.006507965,0.1054582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008454117,"about_ca_system_score_gemma":0.02191023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09002345,"about_ca_topic_score_gemma":0.07574996,"domain_scores_codex":[0.9873862,0.003721187,0.0007897917,0.0005726917,0.00623576,0.001294254],"domain_scores_gemma":[0.9790395,0.00480927,0.0009690724,0.002163085,0.01189322,0.001125802],"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":[0.0001570442,0.0001138583,0.0005989117,0.0002626912,0.000009403998,0.00006496579,0.0004981855,0.0003693367,0.0004031435,0.08252754,0.8939471,0.0210478],"study_design_scores_gemma":[0.00007006509,0.00005250526,0.002940617,0.0007072128,0.00001584594,0.00006306462,0.0005288289,0.0003192757,0.0008306118,0.01838741,0.9760007,0.0000839624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006827877,0.00410792,0.0290649,0.05826521,0.008652216,0.002832196,0.08415703,0.010088,0.7960047],"genre_scores_gemma":[0.05106097,0.003607458,0.09909331,0.0347402,0.001423478,0.009007388,0.08324347,0.009744706,0.708079],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1337594,"threshold_uncertainty_score":0.4474697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984236777496529,"score_gpt":0.2352827317844718,"score_spread":0.1954403640095065,"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."}}