{"id":"W3153191490","doi":"","title":"I❤LA: Compilable Markdown for Linear Algebra","year":2021,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Mathematics, Computing, and Information Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear algebra; Algebra over a field; Mathematics; Computer science; Pure mathematics; Applied mathematics; Mathematical optimization; Geometry","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":[0.001069234,0.001912382,0.001048261,0.001186368,0.0007290599,0.002521411,0.002852576,0.00109376,0.05417068],"category_scores_gemma":[0.005969778,0.001534369,0.001858666,0.001465822,0.0009885111,0.004869099,0.003339904,0.00352172,0.02439588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000974312,"about_ca_system_score_gemma":0.001237093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002231183,"about_ca_topic_score_gemma":0.002891152,"domain_scores_codex":[0.9986387,0.0002441966,0.000177624,0.000379483,0.0003820721,0.0001779593],"domain_scores_gemma":[0.996929,0.001155347,0.0001588367,0.001224525,0.0004129193,0.0001194395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001132097,0.0002989045,0.002064517,0.001951447,0.0002231132,0.0003376399,0.0006900018,0.01143828,0.03088429,0.1097887,0.3029926,0.5381984],"study_design_scores_gemma":[0.0004470311,0.0003278659,0.001032069,0.000400674,0.0002168168,0.000363334,0.0002101999,0.1475122,0.1500626,0.2656679,0.4334731,0.0002862098],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002860862,0.0002885263,0.6013156,0.0002017678,0.0002097009,0.0001053452,0.00394269,0.3845291,0.006546471],"genre_scores_gemma":[0.1520214,0.0006239527,0.6774976,0.001379667,0.00042307,0.001065692,0.02772993,0.1113939,0.02786482],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05417068,"threshold_uncertainty_score":0.181219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05767489961279243,"score_gpt":0.3520611637380513,"score_spread":0.2943862641252589,"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."}}