{"id":"W4225279502","doi":"10.1364/ao.453052","title":"Evaluation of a Gaussian dispersion transformation technique for tomographic mapping of the concentration field of atmospheric chemicals using multi-path optical remote sensing","year":2022,"lang":"en","type":"article","venue":"Applied Optics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Calgary","keywords":"Gaussian; Plume; Sensitivity (control systems); Algorithm; Smoothness; Regularization (linguistics); Dispersion (optics); Transformation (genetics); Optics; Computer science; Mathematics; Physics; Mathematical analysis; Meteorology; Chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008701842,0.0004908553,0.0002347904,0.0006304552,0.0002147325,0.0004297086,0.0004199861,0.0004784738,0.000507973],"category_scores_gemma":[0.001375416,0.0001741065,0.0003745897,0.0006986258,0.0003837885,0.0006561389,0.0003899606,0.0004126541,0.0001174277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005967023,"about_ca_system_score_gemma":0.001081789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006772105,"about_ca_topic_score_gemma":0.006848407,"domain_scores_codex":[0.9997115,0.00006459282,0.000009485041,0.00003423182,0.0001589724,0.00002114511],"domain_scores_gemma":[0.9995139,0.0001949831,0.00004414784,0.0000507478,0.0001724911,0.00002367717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004411782,0.0002461649,0.006612299,0.0001942172,0.0001072854,0.0001588058,0.0001905967,0.4725061,0.1734434,0.006426339,0.001090004,0.3385835],"study_design_scores_gemma":[0.00001533094,0.00006568961,0.0009276345,0.000002667607,0.000008774918,0.00003664566,0.0000189686,0.974887,0.02320634,0.0002766121,0.0005429753,0.00001140708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2494806,0.0002908987,0.747236,0.0002871437,0.00003352833,0.00007525321,0.00009620233,0.0008694693,0.001630937],"genre_scores_gemma":[0.5003083,0.0002091767,0.4983855,0.00002738881,0.000008451176,0.00003628448,0.0001912124,0.00008055609,0.0007531761],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006772105,"threshold_uncertainty_score":0.01346534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649931829271493,"score_gpt":0.2451419482973739,"score_spread":0.228642630004659,"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."}}