{"id":"W4289998958","doi":"","title":"The 3D digital SiPM for nEXO","year":2016,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Silicon photomultiplier; Computer science; Detector; Telecommunications","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":[0.0006540783,0.0004596931,0.0002975488,0.0004858173,0.001080484,0.003018001,0.0005209285,0.001015407,0.06760597],"category_scores_gemma":[0.001049763,0.0002474168,0.0003701845,0.000377028,0.00081619,0.001888924,0.004072344,0.0009290154,0.007311605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005172443,"about_ca_system_score_gemma":0.001007826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001305089,"about_ca_topic_score_gemma":0.002209531,"domain_scores_codex":[0.9996902,0.00005699746,0.000007836497,0.00003658868,0.0001691437,0.0000392613],"domain_scores_gemma":[0.9997709,0.00004710474,0.000006229046,0.00007302595,0.00004591615,0.00005686828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005777655,0.0001400925,0.001952376,0.0005614884,0.00002720685,0.00115743,0.004748156,0.01756811,0.02458514,0.3310466,0.1503228,0.4673129],"study_design_scores_gemma":[0.00002430119,0.00008572123,0.0009532269,0.0002123935,0.00001131972,0.0004375059,0.001267737,0.01774142,0.004913816,0.02026681,0.9540461,0.00003962616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05403994,0.001169972,0.1802777,0.004211301,0.002410644,0.0003847666,0.003096592,0.005912433,0.7484966],"genre_scores_gemma":[0.453286,0.00163825,0.1614306,0.001212033,0.0003870511,0.0006022638,0.003899234,0.004111881,0.3734328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06760597,"threshold_uncertainty_score":0.2261645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916065789952425,"score_gpt":0.2149004350265815,"score_spread":0.1957397771270572,"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."}}