{"id":"W3090921718","doi":"10.1117/12.2583450","title":"Hyperspectral VIS/SWIR wide-field imaging for ink analysis","year":2020,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Photon Etc (Canada)","funders":"","keywords":"Hyperspectral imaging; Counterfeit; Inkwell; Reflectivity; Identification (biology); Remote sensing; Fidelity; Computer science; Field (mathematics); Camouflage; Artificial intelligence; Geology; Optics; Archaeology; Geography; Physics; Telecommunications; Mathematics","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":[0.0008743753,0.0007133153,0.0003095764,0.001697413,0.0004508615,0.0008489366,0.0005335194,0.0006704936,0.005592451],"category_scores_gemma":[0.000810638,0.0003822848,0.0004202962,0.001413393,0.0004567093,0.001371204,0.000562352,0.0009031601,0.001779588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003990297,"about_ca_system_score_gemma":0.0004973272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001336296,"about_ca_topic_score_gemma":0.004453514,"domain_scores_codex":[0.9994658,0.0001079561,0.00002282254,0.00009152127,0.0002560211,0.00005588349],"domain_scores_gemma":[0.9994057,0.0001829625,0.00008509749,0.0001110662,0.0001818301,0.00003331944],"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":[0.0001511049,0.0001609024,0.00410081,0.0003199021,0.00006542067,0.0001126777,0.0001461561,0.004767114,0.8696722,0.002105556,0.003643139,0.114755],"study_design_scores_gemma":[0.00002651818,0.0002117553,0.02521816,0.00009748896,0.00008399962,0.0006884984,0.0003895165,0.1615545,0.7838063,0.004985761,0.02277802,0.000159585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2971973,0.003878485,0.6635296,0.0008743939,0.000345984,0.0002423873,0.002183794,0.005610169,0.02613783],"genre_scores_gemma":[0.3776905,0.001843649,0.6134999,0.0003436026,0.0001089619,0.0001477898,0.001259465,0.000488151,0.004617991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005592451,"threshold_uncertainty_score":0.01870865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651917348528195,"score_gpt":0.2668738780620624,"score_spread":0.2403547045767804,"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."}}