{"id":"W84174504","doi":"","title":"Use of off-line dynamic programming for efficient image interpretation","year":2003,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Operator (biology); Computer science; Interpretation (philosophy); Rank (graph theory); Sequence (biology); Dynamic programming; Artificial intelligence; Domain (mathematical analysis); Machine learning; Algorithm; Mathematics; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0002902047,0.00006908237,0.0000913928,0.00008492047,0.00004842492,0.0001082259,0.0001511985,0.00002282582,0.00001338823],"category_scores_gemma":[0.0002848478,0.00006136044,0.00005753667,0.0002081316,0.00002663751,0.0002401941,0.00002685192,0.00004341503,0.000009370989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002229972,"about_ca_system_score_gemma":0.00003146029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003647247,"about_ca_topic_score_gemma":0.000005157178,"domain_scores_codex":[0.999282,0.00004666432,0.000209664,0.0001850848,0.0001292599,0.0001473474],"domain_scores_gemma":[0.9993861,0.0001598514,0.00009695409,0.0001861094,0.0001308641,0.00004013618],"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":[0.00002631535,0.0002005432,0.00009441988,0.00005292254,0.00002154352,0.000001672637,0.002376712,0.02437566,0.009426838,0.1833945,0.00008152135,0.7799474],"study_design_scores_gemma":[0.0002352162,0.00008468894,0.000157616,0.00001371359,0.000003363191,0.000002458807,0.0001012712,0.9892287,0.001866801,0.0001859826,0.008043527,0.00007665201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01761045,0.00002432079,0.9809827,0.0001016168,0.0001323384,0.0002619573,6.260081e-7,0.00008718129,0.0007988629],"genre_scores_gemma":[0.5507482,0.000001125857,0.4487011,0.00006800389,0.000001848106,0.00001139439,0.000002235298,0.000004105451,0.0004619387],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.964853,"threshold_uncertainty_score":0.2502206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0255024049463968,"score_gpt":0.2860913823671901,"score_spread":0.2605889774207933,"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."}}