{"id":"W6958772817","doi":"10.7916/d8-yb5b-yw66","title":"Emma Gendron","year":2015,"lang":"en","type":"article","venue":"Columbia Academic Commons (Columbia University)","topic":"Particle Accelerators and Free-Electron Lasers","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Movie theater; Nothing; Biography; Scripting language; Public figure","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001600187,0.0001873952,0.0003113284,0.0001485658,0.0002180377,0.0001486022,0.0006659363,0.0003290333,0.0002142503],"category_scores_gemma":[0.00003813993,0.0003740248,0.000112733,0.001032674,0.0001403659,0.0005026512,0.000181283,0.0008157932,0.000231631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819449,"about_ca_system_score_gemma":0.000127825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003911744,"about_ca_topic_score_gemma":0.03345646,"domain_scores_codex":[0.9983507,0.00008271098,0.0002604024,0.0003376969,0.0002654006,0.0007030317],"domain_scores_gemma":[0.9987289,0.00005128144,0.0000570791,0.0004679203,0.00008302478,0.0006117949],"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":[0.00004144104,0.00008533765,0.1547218,0.00003243877,0.0001896776,0.0003520168,0.0004882595,0.002632294,0.005470502,0.0006261839,0.8120229,0.02333718],"study_design_scores_gemma":[0.001597985,0.0001605873,0.01374439,0.00003040562,0.0001008939,0.00004658115,0.0006828182,0.01206491,0.0007413554,0.0006161864,0.9694293,0.0007845921],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9160184,0.0004176056,0.0002500795,0.0000821174,0.0004295636,0.0002776611,0.00002689297,0.0008592745,0.08163835],"genre_scores_gemma":[0.9905779,0.0001271196,0.0001522176,0.00007604593,0.0001076286,0.000004266266,0.00001114911,0.00005513791,0.008888531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1574064,"threshold_uncertainty_score":0.9998712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03021499133176245,"score_gpt":0.1984699808206553,"score_spread":0.1682549894888928,"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."}}