{"id":"W1522576837","doi":"10.1007/978-3-642-03641-5_6","title":"Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scale (ratio); Key (lock); Hidden Markov model; Artificial intelligence; Hierarchical database model; Theoretical computer science; Algorithm; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006529317,0.0006235506,0.0008074943,0.0006891776,0.0003785783,0.0007058544,0.001109804,0.001109646,0.00474693],"category_scores_gemma":[0.00147001,0.0005837125,0.0007519001,0.001028766,0.0006412997,0.00147736,0.001488246,0.001360522,0.001021828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005858721,"about_ca_system_score_gemma":0.0006045924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002616175,"about_ca_topic_score_gemma":0.004179351,"domain_scores_codex":[0.9997998,0.00005469324,0.0000109077,0.00003793871,0.00007198718,0.00002470245],"domain_scores_gemma":[0.9994621,0.0002383004,0.00003343987,0.0001654746,0.00007054648,0.00003018685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002774159,0.0001001061,0.0003861099,0.0002093971,0.0001142726,0.00009937493,0.0001017544,0.4238369,0.02380991,0.1362554,0.008389935,0.4064194],"study_design_scores_gemma":[0.000009741129,0.0000101192,0.0000637034,0.000005218836,0.00000608016,0.000024405,0.000005143343,0.9671166,0.002206666,0.02930666,0.001239408,0.000006296409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002459605,0.0001495594,0.9962676,0.00006344255,0.0000262982,0.00001168444,0.00005965995,0.0003190662,0.0006431782],"genre_scores_gemma":[0.131716,0.0005659439,0.8605976,0.0001205886,0.00009595751,0.00008030683,0.0005174904,0.0002884608,0.006017724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00474693,"threshold_uncertainty_score":0.01588011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03602701384385946,"score_gpt":0.2947614573454064,"score_spread":0.258734443501547,"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."}}