{"id":"W2968755011","doi":"10.1177/0003702819871330","title":"An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy","year":2019,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Integrative Activities; McGill University","keywords":"Background subtraction; Spectroscopy; Optics; Diffraction; Subtraction; Wavelet; Tree (set theory); Materials science; Physics; Analytical Chemistry (journal); Computer science; Nuclear magnetic resonance; Chemistry; Mathematics; Artificial intelligence; Mathematical analysis; Pixel; Astronomy; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149521,0.001259065,0.0008599173,0.00184444,0.0005505476,0.001128772,0.002472737,0.001156172,0.009333779],"category_scores_gemma":[0.002939643,0.0007215456,0.001168521,0.001552469,0.0004308739,0.001275726,0.001753596,0.001636505,0.004583089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004643381,"about_ca_system_score_gemma":0.001436756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001460826,"about_ca_topic_score_gemma":0.002237438,"domain_scores_codex":[0.9989344,0.00008732175,0.00006619013,0.0002413492,0.0006041473,0.00006651545],"domain_scores_gemma":[0.9989263,0.0002238187,0.00009317127,0.0001821467,0.0004986663,0.00007579915],"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":[0.0002875948,0.0002557892,0.001120215,0.0003525708,0.0001322267,0.000279942,0.000213945,0.01736868,0.1043054,0.00822017,0.01993318,0.8475302],"study_design_scores_gemma":[0.0001324075,0.0001475702,0.001798102,0.00004959244,0.00005275333,0.0007259027,0.0000517399,0.8071989,0.1043052,0.008301248,0.07710841,0.0001281315],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001843393,0.00004663129,0.9834345,0.00004327559,0.00004400934,0.00004836072,0.000178477,0.01353447,0.0008268708],"genre_scores_gemma":[0.01565189,0.00008696354,0.9780439,0.00006434636,0.00002461855,0.0001717049,0.001028455,0.002301766,0.002626332],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009333779,"threshold_uncertainty_score":0.03122461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238620232722104,"score_gpt":0.3659564246855104,"score_spread":0.3535702223582893,"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."}}