{"id":"W4285175408","doi":"10.18653/v1/2022.csrr-1","title":"Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)","year":2022,"lang":"en","type":"paratext","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Defense Advanced Research Projects Agency; Department of Foreign Affairs and Trade, Australian Government","keywords":"Commonsense reasoning; Computer science; Commonsense knowledge; Representation (politics); Artificial intelligence; Natural language processing; Cognitive science; Knowledge representation and reasoning; Psychology","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":[0.01154231,0.001988903,0.002695126,0.003063421,0.001507423,0.01028864,0.004482278,0.003601573,0.07703213],"category_scores_gemma":[0.01832212,0.001190778,0.002752272,0.003774714,0.002288197,0.01257618,0.007822751,0.00672815,0.03710396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003223469,"about_ca_system_score_gemma":0.003462184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007782877,"about_ca_topic_score_gemma":0.01021416,"domain_scores_codex":[0.9935281,0.003151371,0.0004247224,0.001092943,0.001406146,0.0003967405],"domain_scores_gemma":[0.9860087,0.006363834,0.0002449044,0.003623725,0.002566979,0.001191789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004375164,0.0003895828,0.0002651632,0.0007801924,0.0001466255,0.0003279705,0.0006491867,0.00246451,0.002518165,0.04540567,0.6747797,0.2718357],"study_design_scores_gemma":[0.00008415879,0.00006632167,0.0005890631,0.0004693235,0.00008448778,0.0003294281,0.0002775108,0.01017099,0.002363591,0.05864453,0.9268698,0.00005079669],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.009930714,0.04213626,0.6702588,0.03791394,0.0304609,0.001156479,0.01037027,0.01419899,0.1835738],"genre_scores_gemma":[0.06159906,0.02753291,0.4721585,0.00918714,0.009707154,0.001555804,0.0715094,0.01022179,0.3365283],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07703213,"threshold_uncertainty_score":0.2576982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852492548777432,"score_gpt":0.2764844724724678,"score_spread":0.2479595469846935,"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."}}