{"id":"W4400015039","doi":"10.14236/ewic/eva2024.47","title":"Reimagining Living Ontologies: An immersive cross-disciplinary collaborative performance that combines biophysical data, generative patterns and improvisation","year":2024,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Improvisation; Cross disciplinary; Computer science; Generative grammar; Human–computer interaction; Ontology; Data science; Artificial intelligence; Visual arts; Art; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003890644,0.0002825385,0.0003615648,0.0004148643,0.0006909604,0.001978427,0.0009903521,0.0001178444,0.00003656699],"category_scores_gemma":[0.0005846816,0.0002096113,0.00004039117,0.001824383,0.0002502839,0.002919453,0.001408507,0.000924916,0.00002054771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003602763,"about_ca_system_score_gemma":0.0007820416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002561406,"about_ca_topic_score_gemma":0.0009268,"domain_scores_codex":[0.9958285,0.0005615246,0.0005963901,0.001286118,0.0008520462,0.0008754206],"domain_scores_gemma":[0.9962662,0.002386283,0.0001820919,0.0006898289,0.0003635497,0.000112085],"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":[0.0007877314,0.0004550751,0.401386,0.0002427158,0.0002315865,0.0002263618,0.07493603,0.0220801,0.005864815,0.01027263,0.002367252,0.4811497],"study_design_scores_gemma":[0.0002255965,0.0005490683,0.07107009,0.0004138179,0.000008331842,0.00002020668,0.01636272,0.9082619,0.0005669947,0.002130677,0.0001055633,0.0002850674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731824,0.001047243,0.02261816,0.001791641,0.0005572895,0.0003929598,0.00002869147,0.00008195591,0.0002996737],"genre_scores_gemma":[0.9983929,0.00009205646,0.0005795516,0.00004896098,0.0004629259,0.00001997094,0.00008324893,0.00002410517,0.0002962369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8861818,"threshold_uncertainty_score":0.9990576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09631980824551052,"score_gpt":0.439910739317379,"score_spread":0.3435909310718684,"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."}}