{"id":"W2613510030","doi":"10.1088/1748-0221/12/05/p05009","title":"Tracking within Hadronic Showers in the CALICE SDHCAL prototype using a Hough Transform Technique","year":2017,"lang":"en","type":"article","venue":"Journal of Instrumentation","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Secretaría de Estado de Investigación, Desarrollo e Innovación; Centre National de la Recherche Scientifique; Fonds Wetenschappelijk Onderzoek; Fonds De La Recherche Scientifique - FNRS; Bundesministerium für Bildung und Forschung; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; National Research Foundation; CERN; Alexander von Humboldt-Stiftung","keywords":"Granularity; Hadron; Tracking (education); Hough transform; Physics; Calorimeter (particle physics); Track (disk drive); Energy (signal processing); Computer science; Nuclear physics; Optics; Artificial intelligence; Image (mathematics); Detector","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":[0.0008010853,0.0002539604,0.0003412633,0.0005334314,0.0004038078,0.001041539,0.0008951779,0.00058703,0.003562179],"category_scores_gemma":[0.0009482716,0.0002498009,0.0002492378,0.000790073,0.0005255668,0.000517594,0.0007114643,0.0006543457,0.0005496534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006944633,"about_ca_system_score_gemma":0.0006745312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004271245,"about_ca_topic_score_gemma":0.006639675,"domain_scores_codex":[0.9995273,0.00004329924,0.00001614077,0.00009098368,0.0002690908,0.00005331259],"domain_scores_gemma":[0.9994258,0.0001208915,0.00004406345,0.0001674879,0.0001811843,0.00006058077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003401096,0.0006121211,0.04848328,0.0005209064,0.0002065796,0.001708813,0.00169357,0.1295525,0.6168031,0.009761414,0.01010445,0.1771523],"study_design_scores_gemma":[0.0002147241,0.001266426,0.07174324,0.00004392865,0.00008398138,0.001016258,0.0006037308,0.378267,0.518666,0.001099555,0.026825,0.0001701368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8116925,0.0001928789,0.1528801,0.0002329225,0.00009346415,0.0002265655,0.001648935,0.007460742,0.02557186],"genre_scores_gemma":[0.9178495,0.0000506754,0.07699791,0.00004733324,0.000008152734,0.00006599769,0.001264659,0.000178809,0.003536936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004271245,"threshold_uncertainty_score":0.0119167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03881848942431675,"score_gpt":0.323303502755417,"score_spread":0.2844850133311002,"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."}}