{"id":"W4402991917","doi":"10.1016/j.rse.2024.114435","title":"Deployment-invariant probability of detection characterization for aerial LiDAR methane detection","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Remote sensing; Lidar; Software deployment; Computer science; Environmental science; Aerial survey; Methane; Artificial intelligence; Geology","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":[0.002527416,0.0008642277,0.0005263875,0.001136006,0.0003072922,0.0007447231,0.001432917,0.0007132898,0.0009117924],"category_scores_gemma":[0.01203995,0.000460757,0.0007966643,0.0008232716,0.0009901815,0.001802406,0.001049902,0.001209621,0.0002209545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716385,"about_ca_system_score_gemma":0.00082176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007084725,"about_ca_topic_score_gemma":0.004391411,"domain_scores_codex":[0.9982986,0.0003879982,0.00006709753,0.000429828,0.0005919403,0.0002245416],"domain_scores_gemma":[0.9923029,0.004314298,0.001502361,0.000844717,0.0008813023,0.0001545038],"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":[0.00008533175,0.00007158458,0.01224119,0.00006794325,0.00005018318,0.0001329151,0.00008990423,0.9486965,0.006080574,0.01276096,0.0007496722,0.01897318],"study_design_scores_gemma":[0.00000243358,0.00001890181,0.001686345,0.000002705416,0.000004733997,0.00004280215,0.00001074825,0.9957401,0.0008886498,0.001393599,0.0002002047,0.00000879109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08782774,0.0001309117,0.9089283,0.0001457839,0.00002368803,0.00006890352,0.0002553086,0.0004283238,0.002190995],"genre_scores_gemma":[0.9629844,0.0001684669,0.03430717,0.00007808187,0.0000216936,0.00009855528,0.000530547,0.000101598,0.001709509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007084725,"threshold_uncertainty_score":0.01408696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024746867577316,"score_gpt":0.2063985495043602,"score_spread":0.196151080828587,"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."}}