{"id":"W4408435786","doi":"10.5194/egusphere-egu25-8355","title":"Bridging the Fleet Distribution Data Gap with Satellite Imagery and Deep Learning for GHG Estimation","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Bridging (networking); Satellite imagery; Satellite; Deep learning; Remote sensing; Greenhouse gas; Environmental science; Distribution (mathematics); Satellite image; Estimation; Computer science; Meteorology; Geography; Artificial intelligence; Oceanography; Engineering; Geology; Aerospace engineering; Mathematics; Systems engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003287108,0.001039824,0.0003563996,0.001439199,0.0002113436,0.0005317504,0.0006123115,0.0006385795,0.0009461553],"category_scores_gemma":[0.001055732,0.00025988,0.0006268462,0.001865942,0.0002875915,0.001289212,0.0004291659,0.000798556,0.0003926292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009604705,"about_ca_system_score_gemma":0.0006085168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06152296,"about_ca_topic_score_gemma":0.06686989,"domain_scores_codex":[0.9997948,0.00002441572,0.00001057892,0.00008147403,0.00005180919,0.00003692821],"domain_scores_gemma":[0.9997788,0.0000520621,0.00004046473,0.00004307259,0.00007452781,0.00001099938],"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":[0.0002767346,0.0001977572,0.06792361,0.0002390422,0.0003984483,0.0003231617,0.00007420997,0.6844992,0.01158262,0.002750084,0.01048429,0.2212509],"study_design_scores_gemma":[0.000009114761,0.00001719628,0.0126379,0.00001997564,0.00003367472,0.00002752359,0.00003935997,0.9781814,0.004656264,0.001891361,0.002470431,0.0000158709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6357206,0.003653122,0.3227721,0.001680775,0.0003706808,0.00008490702,0.01576608,0.006273504,0.01367828],"genre_scores_gemma":[0.9531163,0.0006078233,0.03429968,0.0001779043,0.00005412636,0.00003013278,0.009814011,0.0001400339,0.001760148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06152296,"threshold_uncertainty_score":0.1223297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02620416819836451,"score_gpt":0.2550309095867798,"score_spread":0.2288267413884152,"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."}}