{"id":"W2163622527","doi":"10.1007/978-1-84882-299-3_3","title":"A Variational Approach to the Registration of Tensor-Valued Images","year":2009,"lang":"en","type":"book-chapter","venue":"Advances in pattern recognition","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Compatibility (geochemistry); Tensor field; Tensor (intrinsic definition); Mathematics; Energy functional; Smoothness; Constraint (computer-aided design); Mathematical analysis; Computer science; Artificial intelligence; Applied mathematics; Geometry; Exact solutions in general relativity; Geology","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":[0.000151647,0.0002007989,0.0002863701,0.0001661708,0.00004315324,0.000009555748,0.0001188891,0.0001118384,0.00003305763],"category_scores_gemma":[0.00005562124,0.000163842,0.00009055956,0.00007892003,0.00006389789,0.0001023387,0.00002353025,0.000324298,0.00002307704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006398505,"about_ca_system_score_gemma":0.00003267341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007166801,"about_ca_topic_score_gemma":0.000008214714,"domain_scores_codex":[0.9987325,0.00001678134,0.0004513878,0.0003815928,0.0002899706,0.00012779],"domain_scores_gemma":[0.9990142,0.0000614868,0.000337232,0.0003667917,0.0001798645,0.00004041719],"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":[0.00007809055,0.0001506499,0.0001412642,0.0001514604,0.00001556185,0.000006279704,0.00005288628,0.0001054531,0.0002111813,0.006309344,0.0009582898,0.9918196],"study_design_scores_gemma":[0.002152293,0.001048182,0.009610434,0.003638064,0.0005013768,0.0003901741,0.00006274899,0.002032456,0.002508165,0.5286727,0.4481296,0.001253872],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0001023764,0.0007371436,0.3359111,0.002321486,0.00008100196,0.002038582,0.0003060329,0.0001289811,0.6583733],"genre_scores_gemma":[0.1826034,0.01630368,0.5598065,0.01602969,0.002944662,0.00217151,0.01088937,0.0004865959,0.2087646],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9905657,"threshold_uncertainty_score":0.6681282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0761410596768305,"score_gpt":0.3394263374936108,"score_spread":0.2632852778167803,"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."}}