{"id":"W1535111017","doi":"10.1007/978-3-642-12297-2_42","title":"Better Correspondence by Registration","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Outlier; Pattern recognition (psychology); Similarity (geometry); Computer vision; Pixel; Detector; Image (mathematics); Image registration","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":[0.0007915102,0.0005163639,0.0004260184,0.0005286303,0.0002612692,0.0006592995,0.003748559,0.0004966756,0.00004091977],"category_scores_gemma":[0.0001870417,0.0004792879,0.000111963,0.0005520823,0.0008860195,0.001414639,0.0009464471,0.001523834,0.0000696783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844933,"about_ca_system_score_gemma":0.0003811878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001221914,"about_ca_topic_score_gemma":0.00002560934,"domain_scores_codex":[0.9961922,0.00002601387,0.0005160434,0.001591165,0.001071624,0.0006030154],"domain_scores_gemma":[0.9970264,0.000373013,0.000370362,0.001776515,0.0002826931,0.000171039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006756539,0.00001889388,0.00001892226,0.00001577171,0.000003343588,0.00008058557,0.0001375377,0.00007371737,0.01237306,0.007141332,0.0003744297,0.9797556],"study_design_scores_gemma":[0.0002654242,0.0003941701,0.00004863891,0.0003819276,0.00000829874,0.0001730457,5.721843e-8,0.04042826,0.248787,0.6372985,0.07080884,0.00140586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002609891,0.0004085903,0.992852,0.001194486,0.001190017,0.0003515946,0.000006995816,0.0003563227,0.00361389],"genre_scores_gemma":[0.01571344,0.0001279274,0.9766669,0.00427565,0.0004738741,0.00001369295,0.00001215713,0.00004612506,0.002670218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9783498,"threshold_uncertainty_score":0.9997659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297909768560314,"score_gpt":0.2665337040152264,"score_spread":0.2535546063296232,"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."}}