{"id":"W4280491136","doi":"10.1002/essoar.10509434.3","title":"The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic-Boreal Methane Emissions","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Chapel; World Wide Web; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006097043,0.0004694828,0.0002163724,0.0007081159,0.0004371473,0.0007620106,0.0002459693,0.000151363,0.0003916557],"category_scores_gemma":[0.001077021,0.0001837753,0.0004475364,0.000703212,0.0001567309,0.0005195645,0.0003496087,0.0001457291,0.00007060654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802101,"about_ca_system_score_gemma":0.0004684129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09237763,"about_ca_topic_score_gemma":0.2199522,"domain_scores_codex":[0.9997739,0.00006063113,0.00001706751,0.00006300372,0.00004965038,0.000035768],"domain_scores_gemma":[0.9996929,0.0001163725,0.00006807053,0.00002912806,0.00006714987,0.00002651328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007343802,0.00003249396,0.9644083,0.00004340793,0.0002333297,0.00005661542,0.0001478983,0.01236528,0.006681761,0.0000989636,0.0001654153,0.01569305],"study_design_scores_gemma":[0.00000655128,0.00002865258,0.9472985,0.00001534686,0.00008166853,0.00005180949,0.0002653872,0.05002798,0.001540203,0.0001045722,0.0005634701,0.00001579698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959995,0.0001608818,0.002492067,0.00002370231,0.000004691583,0.000008153493,0.0005029045,0.00006398837,0.0007441142],"genre_scores_gemma":[0.9952894,0.00007911953,0.00392774,0.00001271364,0.00000422886,0.000009109751,0.0005658122,0.00001048267,0.0001013459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09237763,"threshold_uncertainty_score":0.1836799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546900913477375,"score_gpt":0.2679989164618724,"score_spread":0.2525299073270987,"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."}}