{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001997391,0.00007905169,0.00008250815,0.0003179703,0.0001757183,0.000083919,0.0003992778,0.00008125525,0.000001614424],"category_scores_gemma":[0.0001497337,0.00006564619,0.00001387437,0.0005491592,0.00005416547,0.0004078792,0.000553295,0.00009051774,0.00004385528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003748017,"about_ca_system_score_gemma":0.00001500042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000192185,"about_ca_topic_score_gemma":0.000005444003,"domain_scores_codex":[0.9993268,0.000005860846,0.000127018,0.0002587074,0.00005898104,0.0002226082],"domain_scores_gemma":[0.9995558,0.0001013928,0.00003962317,0.000212458,0.00008514216,0.000005581659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[9.605843e-7,0.000002689718,0.0005929072,0.00001382582,0.00001088124,0.000003128831,0.0001836793,8.734256e-7,0.005315399,0.9366672,0.001746994,0.05546149],"study_design_scores_gemma":[0.0004139598,0.0001464084,0.005465888,0.00007604659,0.00000390832,0.00005164163,0.001572829,0.04681817,0.4323411,0.4825948,0.0300447,0.0004705014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1406967,0.00001048156,0.8428926,0.01170603,0.0002754114,0.0001624327,4.363234e-7,0.003732434,0.0005235029],"genre_scores_gemma":[0.8712956,0.00001130996,0.1277726,0.0001709678,0.00001062063,0.00004763477,0.000001679226,0.000006557839,0.0006830246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7305989,"threshold_uncertainty_score":0.2676974,"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."}}