{"id":"W4414809633","doi":"10.1007/978-3-032-05461-6_17","title":"Meta Subspace Analysis: Understanding Model (Mis)behavior in the Metafeature Space","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Subspace topology; Random forest; Naive Bayes classifier; Feature vector; Entropy (arrow of time); Feature (linguistics); Bayes' theorem; Perspective (graphical); Space (punctuation); Bayesian probability","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002438043,0.0005808393,0.0008464371,0.002166882,0.0003475884,0.001040812,0.005119749,0.0003535901,0.00001165784],"category_scores_gemma":[0.0001249041,0.000404109,0.0004026568,0.003398862,0.0003544248,0.0006187104,0.0007198636,0.001665252,0.000007524151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004403887,"about_ca_system_score_gemma":0.0005265231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008647612,"about_ca_topic_score_gemma":0.0009439312,"domain_scores_codex":[0.9956919,0.0001767128,0.0005028038,0.001781364,0.001247213,0.0006000148],"domain_scores_gemma":[0.9960046,0.0008310103,0.0003479252,0.002597619,0.0001212094,0.00009761618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008468936,0.00007148033,0.0003001369,0.00004517081,0.0004341878,0.00008666956,0.002410145,0.4536465,0.00003460036,0.5031978,0.0001147733,0.03965003],"study_design_scores_gemma":[0.000133639,0.00003557286,0.0003204522,0.00005947662,0.0009933716,0.00001273983,0.00000232127,0.9443535,0.00002381933,0.05302426,0.0006007033,0.0004401055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00001007454,0.001404688,0.9849777,0.006710183,0.0003354386,0.0004384279,0.00001263026,0.000116946,0.005993867],"genre_scores_gemma":[0.5309068,0.000172968,0.4619448,0.003089142,0.0001795877,0.00008055042,0.00006648453,0.00003704556,0.003522557],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5308967,"threshold_uncertainty_score":0.9999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07203944131268429,"score_gpt":0.296219313239371,"score_spread":0.2241798719266866,"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."}}