{"id":"W2138211541","doi":"10.1007/978-3-642-31178-9_10","title":"Learning Good Decompositions of Complex Questions","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Support vector machine; Artificial intelligence; Training set; Classifier (UML); Set (abstract data type); Simple (philosophy); Machine learning; Transformation (genetics); Natural language processing; Data mining; Information retrieval","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"],"consensus_categories":[],"category_scores_codex":[0.000718939,0.0003743522,0.0004867957,0.0007417387,0.0003054215,0.0001932603,0.00247658,0.000237302,0.0000463148],"category_scores_gemma":[0.00006486656,0.000376536,0.0001425379,0.0004587065,0.000490461,0.0005675778,0.001225157,0.000860957,0.00004026507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041512,"about_ca_system_score_gemma":0.0003410654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000398308,"about_ca_topic_score_gemma":0.00003773858,"domain_scores_codex":[0.9970911,0.00005385283,0.0005885885,0.0009322891,0.0007483327,0.0005859051],"domain_scores_gemma":[0.9975921,0.0004331831,0.0003451928,0.001183001,0.0002643288,0.0001821817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001504838,0.00004880148,0.0002578071,0.00004347816,0.00001450687,0.00001324503,0.001059825,0.1520361,0.001119708,0.1769584,0.00000624073,0.6684404],"study_design_scores_gemma":[0.000216161,0.0001616049,0.0006302765,0.0004642496,0.00001793205,0.0001063282,3.005254e-7,0.9003332,0.0008943927,0.09379333,0.00271258,0.0006696316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001373064,0.0004783486,0.9911787,0.0004933352,0.0008500094,0.0002250811,0.000004026013,0.0001711993,0.006461977],"genre_scores_gemma":[0.424209,0.00003068931,0.575045,0.0002302878,0.0002692076,0.000004740771,0.000005537338,0.0000198788,0.0001856702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7482971,"threshold_uncertainty_score":0.9998686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02919150543798983,"score_gpt":0.2716458796138543,"score_spread":0.2424543741758645,"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."}}