{"id":"W3046103404","doi":"","title":"Broad-scale Geospatial Analysis of CH 4 Emissions Using High-Resolution Airborne Hyperspectral Imagery Across Alaska and Western Canada","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Geospatial analysis; Remote sensing; Scale (ratio); Environmental science; High resolution; Physical geography; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000229434,0.0001928262,0.0002810476,0.00001119201,0.0002850092,0.00002567209,0.0001652132,0.0000933265,0.00004113233],"category_scores_gemma":[0.0000371989,0.0001887879,0.00006453833,0.0003029632,0.0004280487,0.0001560183,0.0002189093,0.0001353099,0.000007330264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000364721,"about_ca_system_score_gemma":0.000035258,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8736838,"about_ca_topic_score_gemma":0.8164437,"domain_scores_codex":[0.9983598,0.00003618858,0.0003645062,0.0004032194,0.0003809374,0.0004553334],"domain_scores_gemma":[0.9992493,0.00005684773,0.0002403979,0.0002469074,0.000009905883,0.0001966163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001734971,0.0000574307,0.731589,0.000005078226,0.0000756179,0.000009610327,0.0005675266,0.2574007,0.009072256,3.150789e-7,0.00003929638,0.001165869],"study_design_scores_gemma":[0.0001736065,0.00004389864,0.9543829,0.00002140951,0.0002039532,0.000006895475,0.000549474,0.04332103,0.001003965,0.000005807512,0.00008663745,0.0002004133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985682,0.00002986021,0.0005588934,0.00008083982,0.0001122379,0.00008300242,0.00002132603,0.00002155747,0.0005240683],"genre_scores_gemma":[0.9871274,0.00001671571,0.01247101,0.00009217644,0.00006540272,0.000001788865,0.00001368696,0.00001868143,0.0001931509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.222794,"threshold_uncertainty_score":0.7698548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007718877784830667,"score_gpt":0.2251885647422148,"score_spread":0.2174696869573842,"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."}}