{"id":"W4396919126","doi":"10.1088/1742-6596/2743/1/012069","title":"FEBIAD ionization development via a web-app for multidimensional characterization","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; TRIUMF","funders":"","keywords":"Characterization (materials science); Computer science; Development (topology); World Wide Web; Materials science; Nanotechnology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001036018,0.0001051677,0.0001505111,0.00009151742,0.0001130683,0.0001058388,0.00007124939,0.00003507629,0.0000977034],"category_scores_gemma":[0.000009490486,0.00008958745,0.00008315872,0.0001752255,0.00003484885,0.0005648293,0.00001540548,0.0001118119,0.00001341775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000301551,"about_ca_system_score_gemma":0.0002608784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.3204e-7,"about_ca_topic_score_gemma":5.959896e-7,"domain_scores_codex":[0.9993128,0.00001326888,0.0003105787,0.0001049857,0.0001554853,0.0001029484],"domain_scores_gemma":[0.9993029,0.00002934813,0.0001997314,0.00006365951,0.0003694427,0.00003488876],"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":[0.0000439022,0.00006021214,0.001093342,0.00004423864,0.0001492752,0.000001079423,0.0008420653,0.0003728791,0.1523575,0.1192067,0.0003529178,0.7254758],"study_design_scores_gemma":[0.0006452234,0.0002117936,0.004506062,0.0002161433,0.00005483926,0.00001419118,0.0007084483,0.02928177,0.8169317,0.01529798,0.1317585,0.0003732987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3316936,0.00002534158,0.6668937,0.0004568826,0.000659687,0.0001129228,0.00001767698,0.00006161848,0.00007857175],"genre_scores_gemma":[0.9942518,0.000007670645,0.004876481,0.00001451731,0.0003515307,0.00001158125,0.00002078264,0.00001166647,0.0004539352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7251025,"threshold_uncertainty_score":0.365327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613475831095198,"score_gpt":0.2445716930169899,"score_spread":0.228436934706038,"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."}}