{"id":"W4413213677","doi":"10.1016/j.jqsrt.2025.109603","title":"Atmospheric ethylene (C <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si82.svg\" display=\"inline\" id=\"d1e455\"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> H <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si110.svg\" display=\"inline\" id=\"d1e463\"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> ) observations from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS)","year":2025,"lang":"lv","type":"article","venue":"Journal of Quantitative Spectroscopy and Radiative Transfer","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Algorithm; Scalable Vector Graphics; Computer graphics (images); Combinatorics; Mathematics; Computer science; World Wide Web","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":[0.0002148558,0.0003936082,0.0002135603,0.0008795065,0.0004598112,0.0007537837,0.0005007925,0.000587382,0.05134567],"category_scores_gemma":[0.00033438,0.0002362795,0.0003391928,0.001492183,0.0000982983,0.0008689484,0.0002842633,0.0007048115,0.01301721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008693136,"about_ca_system_score_gemma":0.0005070447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02444529,"about_ca_topic_score_gemma":0.02871981,"domain_scores_codex":[0.9998564,0.000007975711,0.000006417177,0.00003970294,0.00007726834,0.00001231887],"domain_scores_gemma":[0.9998677,0.00002412219,0.00001998072,0.00002370644,0.00004600528,0.00001847933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009596305,0.0004258905,0.02607275,0.001022558,0.0002230194,0.0002828294,0.0002235859,0.004919589,0.1330005,0.01203898,0.6855931,0.1352376],"study_design_scores_gemma":[0.0002092906,0.00007475242,0.07079533,0.00005010209,0.00005965241,0.0001173347,0.00005959452,0.004911785,0.08463839,0.00164148,0.8373935,0.00004879388],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08906625,0.0007067195,0.02052927,0.001645008,0.0007479463,0.0003531016,0.6484946,0.01408021,0.2243769],"genre_scores_gemma":[0.2553159,0.001803351,0.0370453,0.001105227,0.0002374083,0.0003854166,0.5180908,0.00481743,0.1811991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05134567,"threshold_uncertainty_score":0.1717684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02097141647243862,"score_gpt":0.2587163452942733,"score_spread":0.2377449288218347,"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."}}