{"id":"W4408473224","doi":"10.5194/egusphere-egu25-31","title":"Developing a method for measuring mercury photoreduction in snow with a LED Solar Simulator ","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Snow; Mercury (programming language); Solar simulator; Simulation; Environmental science; Computer science; Meteorology; Electrical engineering; Engineering; Physics; Photovoltaic system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000752554,0.0002869384,0.0003821581,0.0001487211,0.0000978167,0.00007130426,0.000232198,0.0002430528,0.0001999055],"category_scores_gemma":[0.0001171676,0.0002602575,0.00008120474,0.000206293,0.00006044936,0.0001670298,0.0005000946,0.0002156651,0.00001590175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045126,"about_ca_system_score_gemma":0.000145296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0029033,"about_ca_topic_score_gemma":0.002423036,"domain_scores_codex":[0.9982343,0.0001127994,0.0003787626,0.0007202653,0.0002538494,0.0003000006],"domain_scores_gemma":[0.9993338,0.0001004338,0.0001664349,0.000330412,0.00002235937,0.00004658194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002199488,0.0003063791,0.1924162,0.00251374,0.0006754831,0.00002067515,0.003500542,0.5564983,0.1444891,0.002267185,0.0006700088,0.09444286],"study_design_scores_gemma":[0.00407246,0.0001569783,0.05431024,0.002504193,0.0003678676,0.00008310278,0.0007959415,0.1242203,0.7800535,0.02016981,0.01046009,0.002805515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6058307,0.000009924335,0.390891,0.0001658623,0.001101479,0.001270846,0.00001298606,0.0001098753,0.0006073567],"genre_scores_gemma":[0.4213369,0.000009863803,0.5776528,0.00008605839,0.0001026106,0.0005470891,0.00002827333,0.00003118211,0.0002052589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6355643,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700848449995949,"score_gpt":0.2819033120275323,"score_spread":0.2548948275275728,"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."}}