{"id":"W7095710682","doi":"","title":"BMIT-ID (05ID-2) BEAMLINE FRONT END MECHANICAL DESIGN","year":2016,"lang":"en","type":"article","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Front and back ends; Beamline; Light source; Window (computing); Design elements and principles","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003615684,0.001091608,0.001799096,0.001494474,0.00309568,0.002808898,0.003132505,0.002834477,0.4486032],"category_scores_gemma":[0.002275081,0.0009933349,0.00064382,0.002223134,0.0008734214,0.001477166,0.001845053,0.003499422,0.2345704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005533304,"about_ca_system_score_gemma":0.004818255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009368179,"about_ca_topic_score_gemma":0.02767798,"domain_scores_codex":[0.9977778,0.0002200062,0.00004060285,0.0005265548,0.001107496,0.0003276242],"domain_scores_gemma":[0.9974469,0.0002889386,0.0001539698,0.0004552258,0.001098668,0.0005562743],"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":[0.001177342,0.0001660675,0.002211338,0.0008326275,0.0001013337,0.0001290716,0.0001937321,0.001047272,0.05603974,0.03614379,0.850835,0.05112264],"study_design_scores_gemma":[0.000502912,0.0003443904,0.004183596,0.0001691618,0.00004957905,0.0001710067,0.0001368749,0.006554851,0.03581169,0.006693105,0.9452413,0.000141521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01361482,0.003315732,0.09173773,0.008893882,0.003946162,0.001658716,0.1456544,0.03432482,0.6968538],"genre_scores_gemma":[0.1782212,0.003408553,0.1713856,0.006975056,0.0007773034,0.005184361,0.1369741,0.02351471,0.4735592],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4486032,"threshold_uncertainty_score":0.7865007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0487527766496712,"score_gpt":0.2098446344914286,"score_spread":0.1610918578417574,"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."}}