{"id":"W1977093483","doi":"10.1142/9789812776136_0002","title":"FRESCO: FLEXIBLE ALIGNMENT WITH RECTANGLE SCORING SCHEMES","year":2007,"lang":"en","type":"article","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Rectangle; Fresco; Computer science; Artificial intelligence; Mathematics; Geometry; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004019314,0.001615902,0.001338065,0.00183756,0.001042383,0.00151428,0.002589141,0.002141762,0.009932573],"category_scores_gemma":[0.01208626,0.0009619793,0.001222721,0.002422074,0.000887008,0.002840732,0.002051674,0.002074812,0.006066989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008477031,"about_ca_system_score_gemma":0.001759858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002232685,"about_ca_topic_score_gemma":0.002716887,"domain_scores_codex":[0.9972094,0.001081648,0.0002032899,0.0004984995,0.0007777149,0.0002295344],"domain_scores_gemma":[0.9972787,0.0009934554,0.0003493771,0.0007474818,0.0004800231,0.0001509147],"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":[0.001657944,0.0002918823,0.0034647,0.0008705262,0.0002285947,0.0006008774,0.0005461219,0.1336856,0.06697087,0.1390377,0.0845148,0.5681303],"study_design_scores_gemma":[0.0003268051,0.0003837676,0.001206553,0.0001167177,0.00005655948,0.0008186308,0.0001081742,0.8342394,0.04542447,0.04569466,0.07137106,0.0002531407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01379139,0.0004632553,0.9629292,0.0001971323,0.0001205698,0.0002030331,0.0006076304,0.01724231,0.004445508],"genre_scores_gemma":[0.07584269,0.0003343572,0.9147674,0.000226136,0.00005158719,0.0004022911,0.001729647,0.003109734,0.003536243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009932573,"threshold_uncertainty_score":0.0332278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707690794776632,"score_gpt":0.2584471713434894,"score_spread":0.2413702633957231,"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."}}