{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005757181,0.0001396366,0.0003056428,0.0001238764,0.00006665193,0.00003731743,0.0002137606,0.00009439186,0.02118536],"category_scores_gemma":[0.00004826877,0.0001008025,0.00009252724,0.00007824576,0.00003730039,0.0001752526,0.00005242038,0.00006546912,0.004105546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000620674,"about_ca_system_score_gemma":0.00001753245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001618468,"about_ca_topic_score_gemma":0.00003547248,"domain_scores_codex":[0.9987761,0.000004597331,0.0005120902,0.0003574718,0.00004026164,0.0003094402],"domain_scores_gemma":[0.9992659,0.00005733637,0.0001612327,0.0003730853,0.00002281104,0.0001196344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007358637,0.0002471941,0.02310236,0.00001632373,0.0001437258,0.00001888729,0.00009277586,0.0002107364,0.0003859819,0.8989044,0.05770887,0.01909519],"study_design_scores_gemma":[0.001587275,0.0002826225,0.01156607,0.00002748092,0.000014577,0.00001044916,0.0000199169,0.03469131,0.002870738,0.07768765,0.8704607,0.0007811409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02866594,0.0009641422,0.8965514,0.003956799,0.00100609,0.0002807623,0.0001620846,0.0001239034,0.06828885],"genre_scores_gemma":[0.9704064,0.0005146256,0.004556066,0.0006209605,0.0002297148,0.0000120476,0.000006189493,0.00002322841,0.02363082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9417404,"threshold_uncertainty_score":0.9966699,"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."}}