{"id":"W2110541347","doi":"10.1109/cgiv.2007.32","title":"Curve reconstruction in the presence of noise","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Planar; Computer science; Heuristic; Noise (video); Neighbourhood (mathematics); Sample (material); Graph; Algorithm; Curve fitting; Artificial intelligence; Mathematics; Theoretical computer science; Machine learning; Computer graphics (images); Mathematical analysis; Physics","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":[0.0006608604,0.00003381842,0.00004101546,0.00006609289,0.00001174236,0.00007031189,0.0006159712,0.00001637844,0.000001792288],"category_scores_gemma":[0.00008648095,0.0000221976,0.00001284935,0.0003561136,0.00005515798,0.0007953774,0.00007862005,0.00004749101,0.000002801305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007138234,"about_ca_system_score_gemma":0.00001593614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003321478,"about_ca_topic_score_gemma":0.0000239006,"domain_scores_codex":[0.9995446,0.00001542967,0.000128637,0.0001016673,0.0001203892,0.00008927055],"domain_scores_gemma":[0.9995587,0.00009800396,0.00004247451,0.0002454384,0.00004498668,0.0000103631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002804446,0.00005847208,0.005852071,0.00001158702,9.876468e-7,0.000006724085,0.0006600833,0.000001467525,0.00250145,0.06090497,0.0006461024,0.9293533],"study_design_scores_gemma":[0.000247639,0.0001617328,0.04267352,0.0001566174,0.000002092348,0.0002504842,0.0003440762,0.01802206,0.6004353,0.335977,0.001426207,0.0003032885],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02366184,0.000019036,0.8819885,0.0002683974,0.00004526668,0.00006195501,9.393551e-8,0.00009577921,0.09385919],"genre_scores_gemma":[0.8003231,0.000001602396,0.199495,0.00008149006,0.000004981224,0.000001680575,5.208483e-8,0.000001010713,0.00009114429],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.92905,"threshold_uncertainty_score":0.1144638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914705256276001,"score_gpt":0.2798895862417474,"score_spread":0.2607425336789874,"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."}}