{"id":"W4386631390","doi":"10.1109/oceanslimerick52467.2023.10244474","title":"Developing WaterHCI and OceanicXV technologies for Diving","year":2023,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Australian Research Council","keywords":"Computer science; Sociotechnical system; Underwater; Human–computer interaction; Conceptualization; Architectural engineering; Engineering; Artificial intelligence; Oceanography","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":[0.001551191,0.0005593851,0.000273026,0.0009474118,0.0009162723,0.003211567,0.001776951,0.001317529,0.01108857],"category_scores_gemma":[0.002807746,0.0003958355,0.0005430911,0.0005895773,0.001675045,0.004695624,0.005443835,0.001336143,0.001917958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096133,"about_ca_system_score_gemma":0.001097701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002673884,"about_ca_topic_score_gemma":0.003365977,"domain_scores_codex":[0.999151,0.0001979064,0.0000568432,0.000105119,0.0003669935,0.0001221827],"domain_scores_gemma":[0.9992226,0.0002572311,0.00005628168,0.0001217304,0.0002059688,0.0001361754],"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":[0.0001397366,0.0001291651,0.004279641,0.001920893,0.00003762172,0.001068376,0.02299062,0.00543853,0.07480735,0.3721701,0.02488575,0.4921323],"study_design_scores_gemma":[0.0000424748,0.0004817919,0.004371985,0.001108336,0.00006248843,0.001133426,0.009998635,0.02457655,0.03232345,0.05824032,0.8675032,0.0001574505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03906718,0.001242108,0.8240837,0.00243627,0.0003782755,0.001218782,0.000447351,0.004177529,0.1269488],"genre_scores_gemma":[0.3035269,0.002226387,0.6403713,0.001139296,0.0001258876,0.001834187,0.001119884,0.0008534408,0.0488028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01108857,"threshold_uncertainty_score":0.03709501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104012264936215,"score_gpt":0.311862342947238,"score_spread":0.2608222202978759,"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."}}