{"id":"W7001314883","doi":"","title":"Kenniscompilatie en tellen: een algebraïsche reis","year":2023,"lang":"en","type":"article","venue":"Lirias (KU Leuven)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Imperial College London; Canadian Institute for Advanced Research","keywords":"Heuristics; Probabilistic logic; Set (abstract data type); Task (project management); Inference; Algebraic number; Counting problem; Logic programming","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005917359,0.0002343424,0.0002632815,0.0001917406,0.0001961143,0.0002284533,0.001347353,0.0001642045,0.00008676545],"category_scores_gemma":[0.00009593368,0.0002198733,0.0001199625,0.0009455672,0.00005414043,0.0004980268,0.0005178199,0.0003443735,0.003343045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004451735,"about_ca_system_score_gemma":0.0001248705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005142479,"about_ca_topic_score_gemma":0.000006422486,"domain_scores_codex":[0.9979442,0.0001489279,0.0003398697,0.0006066269,0.0004078469,0.0005525787],"domain_scores_gemma":[0.9984829,0.000258395,0.00006241605,0.0009454465,0.00006501182,0.0001857876],"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":[0.0000379087,0.0002228202,0.00213754,0.0001162445,0.0001841941,0.0003638405,0.01058215,0.00532889,0.005192258,0.4943393,0.1484383,0.3330565],"study_design_scores_gemma":[0.001105312,0.0003703985,0.00677608,0.0001961127,0.00003260644,0.00005888072,0.0002449564,0.7344468,0.004914823,0.118256,0.1321912,0.001406819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262148,0.0003590339,0.8184642,0.01623049,0.002321366,0.0003576135,0.00001781538,0.003051405,0.0329832],"genre_scores_gemma":[0.9727926,0.00008994168,0.01949122,0.001220393,0.000397301,0.00003244497,0.00001869656,0.00003478474,0.005922592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8465778,"threshold_uncertainty_score":0.9974329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328279552440555,"score_gpt":0.2618304424349782,"score_spread":0.2385476469105726,"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."}}