{"id":"W4414091412","doi":"10.3390/rs17183143","title":"Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Coquitlam College; University of Calgary","funders":"","keywords":"Greenhouse gas; Permian; Satellite; Structural basin; Methane; Divergence (linguistics); Climate change","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":[0.001602615,0.0008512509,0.0005101001,0.0011792,0.0004882037,0.0006966166,0.0008593484,0.0009434269,0.0002985664],"category_scores_gemma":[0.002121936,0.0003005771,0.0009692435,0.002416372,0.0004846492,0.0009037042,0.0007860194,0.000413256,0.00006549704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009135716,"about_ca_system_score_gemma":0.0008304201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06466301,"about_ca_topic_score_gemma":0.0533507,"domain_scores_codex":[0.9994842,0.0002177728,0.00003128574,0.0001115348,0.00009700366,0.00005813438],"domain_scores_gemma":[0.9992624,0.0004364502,0.00007409319,0.00006819824,0.0001217169,0.000037163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004071965,0.0004995164,0.3059413,0.0002674696,0.0005483416,0.002324741,0.0006340448,0.6226644,0.008997399,0.0009982171,0.0003822773,0.05633512],"study_design_scores_gemma":[0.00006313066,0.0001788355,0.128693,0.00002851765,0.0001654745,0.0002459094,0.0008436904,0.8623511,0.005969577,0.0007109007,0.0006901127,0.00005977359],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960188,0.000120719,0.003199105,0.00005034538,0.000003127434,0.00001683416,0.000136431,0.00005879436,0.0003958701],"genre_scores_gemma":[0.986685,0.0001183481,0.01259014,0.00001254803,0.00000782623,0.0000185159,0.0003534448,0.0000140405,0.000200031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06466301,"threshold_uncertainty_score":0.1285732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331197127065835,"score_gpt":0.2622556708475301,"score_spread":0.2389436995768718,"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."}}