{"id":"W2396409565","doi":"","title":"A Method of Virtual Camera Selection Using Soft Constraints.","year":2011,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Constraint (computer-aided design); ENCODE; Software; Virtual machine; Computation; Perspective (graphical); Code (set theory); Adaptation (eye); Selection (genetic algorithm); Artificial intelligence; Computer vision; Human–computer interaction; Algorithm; Programming language; Engineering","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":[0.002859838,0.00009856081,0.0001359415,0.00007844702,0.000437757,0.00007363886,0.0005383625,0.00008263563,0.0002745158],"category_scores_gemma":[0.0001088143,0.00007617569,0.0001403218,0.001035862,0.0005515225,0.0003894224,0.0002249648,0.0005016879,0.00001514111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096995,"about_ca_system_score_gemma":0.0004221719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00034971,"about_ca_topic_score_gemma":0.00001255774,"domain_scores_codex":[0.9980801,0.0004720893,0.0002268179,0.0002703191,0.0005979805,0.0003527381],"domain_scores_gemma":[0.9986466,0.0003223532,0.00007848666,0.0003300335,0.0005379824,0.00008453811],"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":[0.00007791065,0.0002134208,0.004177661,0.00006832161,0.0003941744,0.000006095939,0.07296222,0.007313047,0.04938901,0.2717349,0.008203283,0.58546],"study_design_scores_gemma":[0.0004423128,0.0001669703,0.002346371,0.0000259698,0.00001322228,0.00007443321,0.002267304,0.969914,0.01991416,0.003928042,0.0007238367,0.0001833427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009457853,0.00001431939,0.9884664,0.0004634476,0.0001901079,0.0002297026,0.000003283734,0.0000762596,0.001098566],"genre_scores_gemma":[0.6969489,0.00003124922,0.3024786,0.0002407748,0.0001075403,0.00001371194,0.000001197399,0.00001071718,0.0001673434],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.962601,"threshold_uncertainty_score":0.3366917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1238334672819026,"score_gpt":0.3838814007859753,"score_spread":0.2600479335040727,"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."}}