{"id":"W7096875273","doi":"","title":"Author manuscript, published in &amp;quot;NIPS Workshop on Representations and Inference on Probability Distributions, Whistler: Canada (2007)&amp;quot; On Probability Distributions for Trees: Representations, Inference and Learning","year":2008,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tree diagram; Tree (set theory); Probability distribution; Series (stratigraphy); Inference; Class (philosophy); Algebraic number; Tree automaton; Representation (politics)","routes":{"ca_aff":false,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002337527,0.001125379,0.002243027,0.001729009,0.001718557,0.006163072,0.001879744,0.001671318,0.3162049],"category_scores_gemma":[0.01568734,0.0008341331,0.0009586639,0.003403858,0.0008738097,0.004467892,0.001895603,0.00166653,0.1134039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001984245,"about_ca_system_score_gemma":0.001596782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00647736,"about_ca_topic_score_gemma":0.01337504,"domain_scores_codex":[0.9989185,0.0002460311,0.00006284149,0.0003707984,0.0002939758,0.0001077491],"domain_scores_gemma":[0.9937773,0.001933156,0.0001922829,0.0009225584,0.002683959,0.0004907761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003543023,0.00007357271,0.001036714,0.0004309304,0.0001384915,0.0002011509,0.0001434411,0.002011904,0.0008206546,0.01894076,0.8372785,0.1385695],"study_design_scores_gemma":[0.00008813314,0.00006290554,0.002135785,0.0003179644,0.00007605715,0.0003321644,0.0002008551,0.0124808,0.001940059,0.04203999,0.9402534,0.00007189754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01482813,0.03986105,0.2971852,0.1161399,0.2143203,0.0005768987,0.03737374,0.006922666,0.272792],"genre_scores_gemma":[0.05495903,0.01570714,0.07713718,0.007245997,0.01616568,0.0002994433,0.02277805,0.0034873,0.8022203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3162049,"threshold_uncertainty_score":0.9753507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602749537341858,"score_gpt":0.3212853232006663,"score_spread":0.2552578278272477,"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."}}