{"id":"W7063474917","doi":"","title":"Análisis de reparto de mercancías según tipologías de calles","year":2021,"lang":"es","type":"dissertation","venue":"idUS (Universidad de Sevilla)","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Sample (material); Work (physics); Quarter (Canadian coin)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061331,0.0002579125,0.0003394925,0.002817874,0.001273399,0.001953326,0.0008651785,0.0006283644,0.004532215],"category_scores_gemma":[0.004678308,0.0002220367,0.0003760219,0.00293207,0.0005727351,0.001059038,0.001244528,0.0005849024,0.00100012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001590578,"about_ca_system_score_gemma":0.0008122405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04274455,"about_ca_topic_score_gemma":0.06328931,"domain_scores_codex":[0.9986481,0.0001779142,0.00007200472,0.0002303215,0.0006088417,0.0002628275],"domain_scores_gemma":[0.9955406,0.001007835,0.0009826277,0.0002817696,0.001930593,0.0002564888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003367735,0.00007817142,0.8537387,0.0003483849,0.0001278293,0.0005990671,0.02134545,0.002790232,0.01892089,0.002136181,0.003158797,0.09641957],"study_design_scores_gemma":[0.000002005306,0.00006683318,0.9603644,0.00007133344,0.00003806892,0.000205539,0.02459662,0.002325591,0.003183488,0.0003686346,0.00874219,0.00003537406],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839156,0.0003445671,0.002549182,0.0001387115,0.00001344437,0.00005893983,0.0009004314,0.0001110218,0.01196813],"genre_scores_gemma":[0.9881697,0.0002721246,0.002549452,0.00003740923,0.000007579394,0.0000768317,0.000897234,0.00004682093,0.007942929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04274455,"threshold_uncertainty_score":0.08499151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009766525700181064,"score_gpt":0.2654171554418568,"score_spread":0.2556506297416758,"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."}}