{"id":"W3047818613","doi":"10.1007/978-3-030-55190-2_57","title":"Solving Jigsaw Puzzles Using Variational Autoencoders","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Jigsaw; Computer science; Artificial intelligence; Image (mathematics); Simplicity; Transfer of learning; Machine learning; Range (aeronautics); Pattern recognition (psychology); Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000304304,0.0003232479,0.0004481185,0.0002249745,0.0002593212,0.0004004414,0.0003987852,0.0001671253,0.000004494537],"category_scores_gemma":[0.00003216495,0.0003270413,0.0000761925,0.00009614849,0.0000854182,0.0006205018,0.0002732508,0.0004097745,0.000010303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001251985,"about_ca_system_score_gemma":0.000151515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000375916,"about_ca_topic_score_gemma":0.000007582548,"domain_scores_codex":[0.9980018,0.00003772729,0.0006913369,0.0006919756,0.0003098486,0.0002673389],"domain_scores_gemma":[0.9989065,0.0001660774,0.0005281186,0.0002104787,0.0001059803,0.00008282872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004431873,0.000009768957,0.0002014018,0.0007058324,0.00004583317,0.00004157589,0.0009251049,0.08097684,0.00001328655,0.6684888,0.00002856978,0.2485586],"study_design_scores_gemma":[0.00008638488,0.00002200865,0.00000397606,0.001917535,0.00001146235,0.0001921351,0.0000969992,0.9598404,0.00001591403,0.02139562,0.01603877,0.0003788414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000224254,0.02167258,0.9385167,0.00005793262,0.002474971,0.0001772687,0.000002651287,0.0001352734,0.03694013],"genre_scores_gemma":[0.2849785,0.005881444,0.6830803,0.0004974089,0.004004112,0.00001906882,0.00004487792,0.0002357491,0.02125859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8788635,"threshold_uncertainty_score":0.9999182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427687811026191,"score_gpt":0.2606900988102562,"score_spread":0.2364132206999943,"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."}}