{"id":"W2122297881","doi":"10.1007/3-540-47778-0_31","title":"Indentation and Protrusion Detection and Its Applications","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; BC Cancer Agency","funders":"","keywords":"Indentation; Measure (data warehouse); Computer science; Curvature; Artificial intelligence; Scale (ratio); Computer vision; Set (abstract data type); Object (grammar); Space (punctuation); Pattern recognition (psychology); Geometry; Data mining; Mathematics; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036339,0.001033566,0.001024346,0.002094546,0.0004950115,0.001174178,0.00213033,0.001790061,0.01189989],"category_scores_gemma":[0.0007745853,0.00066449,0.0005197426,0.002794049,0.001016892,0.002299436,0.001019171,0.001538294,0.006504529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005162905,"about_ca_system_score_gemma":0.000280448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003116006,"about_ca_topic_score_gemma":0.0004488471,"domain_scores_codex":[0.999395,0.00003896837,0.00001664623,0.0001368478,0.0003749393,0.00003750276],"domain_scores_gemma":[0.9994842,0.0002706481,0.00002955342,0.00007215247,0.0001252141,0.00001821833],"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":[0.00007775072,0.0000845235,0.000398783,0.001067551,0.00002066277,0.0003475475,0.0002439798,0.002134559,0.09148572,0.06474853,0.01733277,0.8220576],"study_design_scores_gemma":[0.00001410122,0.0002990576,0.002307471,0.0003396642,0.00006797762,0.006385328,0.0002547308,0.04033601,0.2290937,0.1000157,0.6207238,0.0001624293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01324778,0.1194709,0.6869267,0.001271199,0.00157781,0.0001960479,0.0004540742,0.002384146,0.1744715],"genre_scores_gemma":[0.1453058,0.0968897,0.4055102,0.001215753,0.001423027,0.0003004951,0.0006670597,0.0006049525,0.3480829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01189989,"threshold_uncertainty_score":0.03980911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04142557262739266,"score_gpt":0.2636037115158488,"score_spread":0.2221781388884561,"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."}}