{"id":"W2396856533","doi":"10.82308/1390","title":"Efficient and reliable methods for direct parameterized image registration","year":2008,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Computer science; Hessian matrix; Image registration; Measure (data warehouse); Context (archaeology); Mathematical optimization; Algorithm; Range (aeronautics); Pixel; Reliability (semiconductor); Image (mathematics); Mathematics; Artificial intelligence; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002296088,0.0003002743,0.0003996728,0.0001880081,0.0008989879,0.0001356223,0.000648518,0.0001652931,0.00002649189],"category_scores_gemma":[0.001985231,0.0002915787,0.000137357,0.0004887352,0.0001974357,0.0007723845,0.0002753814,0.0003125424,0.00002355956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001842021,"about_ca_system_score_gemma":0.00003527782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005996257,"about_ca_topic_score_gemma":0.000002967264,"domain_scores_codex":[0.9971321,0.0004415416,0.0006122812,0.000890193,0.0004358101,0.00048805],"domain_scores_gemma":[0.9976306,0.0007110015,0.0002901291,0.0007830483,0.0002684354,0.0003168044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005081081,0.0001731907,0.0000063395,0.00008020956,0.00003683058,0.00003535001,0.00001578221,0.00001950497,0.5680563,0.0302916,0.00007337148,0.4011607],"study_design_scores_gemma":[0.001216796,0.000327619,0.0001521962,0.00006177485,0.00002926676,0.0001232437,0.00001163056,0.01722106,0.9482356,0.01608011,0.01600451,0.0005362459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1953055,0.00029838,0.7719419,0.000301989,0.0008764557,0.002632562,0.0001424503,0.002234868,0.02626591],"genre_scores_gemma":[0.1165331,0.00009221468,0.881784,0.0006080639,0.00001459134,0.0002452846,0.00001285951,0.00003426828,0.000675623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4006245,"threshold_uncertainty_score":0.9999536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03360948732611684,"score_gpt":0.3152064275849495,"score_spread":0.2815969402588327,"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."}}