{"id":"W4412875516","doi":"10.1145/3711896.3737415","title":"MethaneS2CM: A Dataset for Multispectral Deep Methane Emission Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Methane; Computer science; Remote sensing; Artificial intelligence; Environmental science; Geology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009895429,0.003352582,0.001375934,0.003533241,0.0009288015,0.001279813,0.003444843,0.002554614,0.004200295],"category_scores_gemma":[0.002280691,0.0005301008,0.002014539,0.003851767,0.0006159311,0.001604149,0.002110811,0.001920345,0.006011879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001565857,"about_ca_system_score_gemma":0.001621075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03655355,"about_ca_topic_score_gemma":0.06991658,"domain_scores_codex":[0.9984303,0.0002108474,0.0001741773,0.0005021311,0.0004332878,0.0002492884],"domain_scores_gemma":[0.999248,0.0001364452,0.0001025546,0.0001959229,0.0002290219,0.00008796182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004943821,0.000467756,0.01317065,0.002472559,0.0004781699,0.0005060085,0.000148963,0.01169586,0.00736038,0.001169781,0.9194884,0.04254698],"study_design_scores_gemma":[0.0008017705,0.0003122374,0.06742856,0.001029287,0.0003135719,0.001407762,0.0009640814,0.0761105,0.01281648,0.0051969,0.8331238,0.0004949984],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01267875,0.001801175,0.002648412,0.0004785575,0.0003075741,0.0001838233,0.9753208,0.004953455,0.001627397],"genre_scores_gemma":[0.007616002,0.0002231612,0.004815248,0.0001200439,0.00002868642,0.0001341921,0.9865856,0.00009875908,0.0003783426],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03655355,"threshold_uncertainty_score":0.07268161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006534323963642518,"score_gpt":0.2480337052375225,"score_spread":0.24149938127388,"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."}}