{"id":"W4384937628","doi":"10.1016/j.rsase.2023.101033","title":"Incorporation of neighborhood information improves performance of SDB models","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Canadian Space Agency; Indigenous and Northern Affairs Canada; University of Ottawa","keywords":"Spatial analysis; Computer science; Context (archaeology); Bathymetry; Random forest; Calibration; Lidar; Remote sensing; Parametric statistics; Satellite; Autocorrelation; Convolutional neural network; Environmental science; Artificial intelligence; Statistics; Geography; Cartography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244324,0.0001181236,0.0001520879,0.00003394246,0.000191097,0.00001186223,0.0000813249,0.00008900647,0.000008178657],"category_scores_gemma":[0.000003633151,0.0001199019,0.00007209904,0.0003625945,0.0002882745,0.0002622312,0.0001154735,0.00008812289,0.00006772933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007159013,"about_ca_system_score_gemma":0.0000105054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001650776,"about_ca_topic_score_gemma":0.00000187926,"domain_scores_codex":[0.9990163,0.00001754051,0.0003593705,0.0001960621,0.0002555879,0.0001551631],"domain_scores_gemma":[0.9992866,0.00003880985,0.0002511749,0.0003550983,0.00001192897,0.00005642322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008699287,0.00003315392,0.0002183771,0.00007833637,0.00002422263,4.205845e-8,0.00169317,0.05589129,0.09472127,0.0003065402,0.0004678851,0.846557],"study_design_scores_gemma":[0.0001909915,0.00004598965,0.007234845,0.00001881471,0.00003024421,0.000004219355,0.0004512136,0.9755643,0.006033254,0.004407058,0.005871003,0.0001480882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7571812,0.00002712724,0.2373527,0.0005535831,0.00001836316,0.0005830064,0.00001344931,0.00008740321,0.004183142],"genre_scores_gemma":[0.9593363,0.0009220029,0.03949767,0.00006113318,0.0000155882,0.000001301196,0.00006881438,0.0000106793,0.00008646506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.919673,"threshold_uncertainty_score":0.4889457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009481349559964172,"score_gpt":0.1969394704648258,"score_spread":0.1874581209048616,"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."}}