{"id":"W3112144088","doi":"10.1016/j.pacs.2020.100228","title":"Highly sensitive broadband differential infrared photoacoustic spectroscopy with wavelet denoising algorithm for trace gas detection","year":2020,"lang":"en","type":"article","venue":"Photoacoustics","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Trace gas; Noise reduction; Photoacoustic spectroscopy; Noise (video); Materials science; Wavelet; Optics; Fourier transform infrared spectroscopy; Analytical Chemistry (journal); Photoacoustic imaging in biomedicine; Physics; Acoustics; Chemistry; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005572115,0.0004204843,0.0004132078,0.00006149989,0.000406924,0.0001525289,0.0002111935,0.0002037303,0.0002148367],"category_scores_gemma":[0.00008357215,0.0004124045,0.0001297086,0.0003067355,0.0001337334,0.0001114593,0.00004243084,0.0004413574,0.00001695447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001946531,"about_ca_system_score_gemma":0.0001178235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003993977,"about_ca_topic_score_gemma":0.00001838906,"domain_scores_codex":[0.9979672,0.00001940323,0.0003706673,0.0006936293,0.0003530022,0.0005960861],"domain_scores_gemma":[0.998668,0.0002439039,0.0002055126,0.0003611787,0.0002112113,0.000310207],"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":[0.0003759698,0.0001378779,0.0000173224,0.0001532292,0.0001532047,0.00001649839,0.0005455888,0.000468602,0.9950959,0.00001661655,0.0004671679,0.002551995],"study_design_scores_gemma":[0.001285864,0.0001848227,0.0000250022,0.00003024669,0.0003790425,0.00003606392,0.0004454895,0.3268507,0.6695294,0.000120895,0.0007621102,0.0003502424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2753637,0.00003180109,0.7222024,0.00008464607,0.00008083955,0.0004674352,0.0007193394,0.0002932656,0.0007566024],"genre_scores_gemma":[0.9722303,0.00003261436,0.02564683,0.0002733319,0.0009553637,0.0002428641,0.0002026821,0.0001191196,0.0002969384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6968665,"threshold_uncertainty_score":0.9998328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064167842746249,"score_gpt":0.2317956018441289,"score_spread":0.2211539234166664,"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."}}