{"id":"W6908450745","doi":"10.26092/elib/2423","title":"Advancing airborne remote sensing of CO2 and CH4 emissions from point sources","year":2022,"lang":"en","type":"article","venue":"Media (https://www.suub.uni-bremen.de/)","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SCIAMACHY; Hyperspectral imaging; Greenhouse gas; Differential optical absorption spectroscopy; Satellite; Moderate-resolution imaging spectroradiometer; Atmosphere (unit); Methane","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":[0.0002251028,0.0003291662,0.0001496795,0.0004365649,0.0001476898,0.0004135784,0.0002396581,0.0002760871,0.0003307251],"category_scores_gemma":[0.0002618548,0.0001243912,0.0002426258,0.0004852587,0.0001285264,0.0002892938,0.000246093,0.0003394393,0.0001011091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005866382,"about_ca_system_score_gemma":0.0008229053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05718758,"about_ca_topic_score_gemma":0.09670103,"domain_scores_codex":[0.9998647,0.00001094179,0.000002955581,0.00003894125,0.0000662015,0.00001622831],"domain_scores_gemma":[0.999905,0.00002495551,0.00001107401,0.0000106035,0.0000436714,0.000004694527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003126678,0.0003616531,0.0403626,0.0002892896,0.00008854849,0.0001480853,0.0003456728,0.05712108,0.6974096,0.001148179,0.001270351,0.2011423],"study_design_scores_gemma":[0.0001245769,0.0002733387,0.2888921,0.00005220081,0.0001810093,0.0001340582,0.0004929524,0.4740956,0.2265107,0.00102529,0.008143879,0.00007433765],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819068,0.0004005168,0.01362798,0.00008290575,0.0000176833,0.00003780976,0.0006629908,0.0001821397,0.003081167],"genre_scores_gemma":[0.9520024,0.0004733976,0.04539793,0.00002835162,0.00001582727,0.00002215692,0.001039696,0.00001595772,0.001004343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05718758,"threshold_uncertainty_score":0.1137094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220462326075,"score_gpt":0.2241536098067186,"score_spread":0.2119489865459686,"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."}}