{"id":"W4392617722","doi":"10.1145/3613904.3642001","title":"DeepSee: Multidimensional Visualizations of Seabed Ecosystems","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Ocean Sciences; Schmidt Ocean Institute; Canadian Institute for Advanced Research; National Aeronautics and Space Administration; California Institute of Technology; Jet Propulsion Laboratory; Nuclear Safety and Security Commission; Center for Dark Energy Biosphere Investigations; National Science Foundation","keywords":"Workflow; Software deployment; Sample (material); Context (archaeology); Computer science; Biogeochemical cycle; Earth science; Seabed; Field (mathematics); Data science; Teamwork; Workspace; Sampling (signal processing); Oceanography; Ecology; Geography; Geology; Telecommunications; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002011653,0.0009737697,0.0004936248,0.00162968,0.0006215388,0.002700866,0.001201435,0.0006966119,0.01066527],"category_scores_gemma":[0.006168647,0.0005450243,0.0008736482,0.001060424,0.0006490676,0.002317471,0.00427093,0.001039766,0.00108796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003662823,"about_ca_system_score_gemma":0.0006923371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001667298,"about_ca_topic_score_gemma":0.004179741,"domain_scores_codex":[0.9993634,0.0002876633,0.00004840884,0.00009241611,0.0001650883,0.00004309979],"domain_scores_gemma":[0.9955284,0.002793233,0.0002235698,0.0006936992,0.0003663393,0.0003947015],"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":[0.003390912,0.001156872,0.03818575,0.004974612,0.0006871736,0.001969696,0.05476427,0.08180053,0.1093143,0.04657505,0.09754841,0.5596324],"study_design_scores_gemma":[0.001185441,0.001149705,0.04560637,0.001180339,0.0002844639,0.001140053,0.01362572,0.3940901,0.05502043,0.1133116,0.372856,0.0005497835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1610664,0.001067969,0.7660539,0.001431206,0.0002344339,0.0007111422,0.0181512,0.0390499,0.01223385],"genre_scores_gemma":[0.4676043,0.0008176887,0.514671,0.0002663992,0.00007190237,0.001141894,0.007923897,0.003706673,0.003796332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01066527,"threshold_uncertainty_score":0.03567886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03002611906854111,"score_gpt":0.3327215401889252,"score_spread":0.3026954211203841,"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."}}