{"id":"W4387271805","doi":"10.2139/ssrn.4562699","title":"Comments on 'Canadian Guardrails for Generative AI – Code of Practice'","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Code (set theory); Code of practice; Computer science; Artificial intelligence; Programming language; Engineering; Engineering ethics; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002566597,0.0001145435,0.0001456296,0.0002535236,0.0003808652,0.0001091023,0.0005601115,0.00005884604,0.000004079664],"category_scores_gemma":[0.0002246697,0.0001048851,0.00007841198,0.0003566737,0.00001861984,0.0003444405,0.00003162652,0.0008618316,0.00003866801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004802074,"about_ca_system_score_gemma":0.003371776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008770225,"about_ca_topic_score_gemma":0.005047402,"domain_scores_codex":[0.9976276,0.0001303133,0.000227558,0.0001936524,0.0002378592,0.001582998],"domain_scores_gemma":[0.998952,0.000351526,0.0001835031,0.0002079283,0.0001727697,0.0001322513],"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":[0.0000845568,0.00007266241,0.0004857121,0.00001410025,0.0003788179,0.00002696624,0.002579944,0.006574655,0.0004509267,0.8745868,0.03978986,0.07495503],"study_design_scores_gemma":[0.00276026,0.004328512,0.0003280609,0.0002080427,0.00007263733,0.0004528868,0.001658925,0.09052303,0.003349929,0.6572653,0.2383314,0.0007209965],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007792471,0.0007355571,0.9131369,0.07454922,0.0007365996,0.0003888443,0.000042418,0.0001196399,0.002498335],"genre_scores_gemma":[0.9828182,0.0004307041,0.009521224,0.005266587,0.0002609474,0.00001963693,0.00002245564,0.00002598839,0.001634299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9750257,"threshold_uncertainty_score":0.5981386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901172638986167,"score_gpt":0.3014928401602893,"score_spread":0.2824811137704276,"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."}}