{"id":"W4379251739","doi":"10.48550/arxiv.2306.00104","title":"Teaching Linear Algebra in a Mechanized Mathematical Environment","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mathematics Education and Teaching Techniques","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Python (programming language); Linear algebra; Algebra over a field; Maple; Computer science; Mathematics education; MATLAB; Symbolic computation; Programming language; Mathematics; Pure mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001752732,0.0004771311,0.0003416896,0.0006715853,0.001175882,0.002195987,0.001350165,0.0008505668,0.02712902],"category_scores_gemma":[0.005017459,0.0003755845,0.0005773572,0.0005964683,0.002851839,0.005895577,0.003291382,0.002427781,0.005685077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366279,"about_ca_system_score_gemma":0.002506203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008646955,"about_ca_topic_score_gemma":0.001741125,"domain_scores_codex":[0.9985539,0.0006331339,0.00005783784,0.0001768291,0.0004509765,0.0001271746],"domain_scores_gemma":[0.9979447,0.001157803,0.000132451,0.0003060629,0.0003016335,0.0001575274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001677432,0.0001159745,0.0003564412,0.000179411,0.000008338441,0.0000843048,0.001522442,0.005824284,0.004402855,0.8881397,0.01619942,0.08315017],"study_design_scores_gemma":[0.00002394625,0.0000582795,0.0004420104,0.0002011946,0.000006134131,0.0002031148,0.0006920664,0.01870468,0.004161973,0.7185271,0.256947,0.00003260142],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01377245,0.0003598542,0.8840712,0.006671162,0.0003184518,0.00008498648,0.00009438111,0.002717949,0.09190939],"genre_scores_gemma":[0.2302312,0.001466859,0.7233974,0.002039158,0.0003850691,0.0003842192,0.0002784341,0.0007473573,0.04107031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02712902,"threshold_uncertainty_score":0.09075564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1554897569855435,"score_gpt":0.2693834646561127,"score_spread":0.1138937076705692,"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."}}