{"id":"W3032952342","doi":"10.1139/cjfas-2019-0424","title":"Improving the communication and accessibility of stock assessment using interactive visualization tools","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Computer science; Data science; Variety (cybernetics); Visualization; Stock (firearms); Data visualization; Disparate system; Interactive visualization; World Wide Web; Data mining; Geography; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0006333311,0.00004708035,0.000103984,0.00005936879,0.0002845868,0.0006478832,0.0004652495,0.0000134182,0.000009075581],"category_scores_gemma":[0.0004372046,0.00003155341,0.00001557604,0.0003647468,0.0003350568,0.001424741,0.00006665698,0.00005834695,4.130774e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000213454,"about_ca_system_score_gemma":0.0005900185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001192248,"about_ca_topic_score_gemma":0.0008452508,"domain_scores_codex":[0.99933,0.00009852499,0.0002527552,0.00009146079,0.0001494174,0.00007784663],"domain_scores_gemma":[0.9991937,0.00015025,0.0003425099,0.00009547843,0.00008425763,0.0001338148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001701283,0.00005780477,0.4236111,0.0001940018,0.000102451,0.00001132844,0.05730669,0.0003788628,0.002724409,0.08426235,0.001246757,0.4300872],"study_design_scores_gemma":[0.000130827,0.0001700469,0.02124409,0.00006359575,0.00001523888,0.00001277886,0.004370425,0.9722112,0.0001877696,0.001169965,0.0003534764,0.00007054742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5030061,0.00027529,0.4920439,0.004351995,0.00007676507,0.00007739522,0.000003460999,0.000002864766,0.0001622282],"genre_scores_gemma":[0.9896014,0.00002695582,0.009893299,0.0004617535,0.00001207292,2.192438e-7,5.505598e-7,0.000001282815,0.000002449151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9718324,"threshold_uncertainty_score":0.6247553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08496774250320374,"score_gpt":0.3483614535375278,"score_spread":0.2633937110343241,"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."}}