{"id":"W4293069380","doi":"10.5194/isprs-archives-xliii-b3-2022-633-2022","title":"DETECTION OF MAYA <i>SACBEOB</i> (SACBES) USING OPTICAL AND SAR IMAGERY IN NORTHERN PETÉN, MEXICO","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Archaeology and ancient environmental studies","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Japan Aerospace Exploration Agency; U.S. Geological Survey; European Space Agency","keywords":"Maya; Remote sensing; Satellite imagery; Lidar; Geography; Radar; Satellite; Identification (biology); Cartography; Computer science; Archaeology; Telecommunications; Engineering","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":[0.0001247236,0.0001828993,0.0001162462,0.0007694641,0.0005719521,0.0004130552,0.0001885754,0.0002123335,0.0004637986],"category_scores_gemma":[0.0001829647,0.0001120876,0.00007842188,0.0004972386,0.0002652923,0.0001651306,0.0002616826,0.000153732,0.00007778731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009505044,"about_ca_system_score_gemma":0.0003576446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.200963,"about_ca_topic_score_gemma":0.4671243,"domain_scores_codex":[0.9999479,0.000004860914,0.000002017337,0.0000186952,0.00001093986,0.00001559766],"domain_scores_gemma":[0.9998933,0.00001048538,0.00004086292,0.000005990514,0.00003010463,0.00001916071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004896676,0.0001243329,0.9503702,0.00007564577,0.00007195956,0.0007086883,0.00121001,0.0007199392,0.01899094,0.000307051,0.001786554,0.02514513],"study_design_scores_gemma":[0.000005451734,0.00001517434,0.9965011,0.000007869064,0.00001206826,0.00003777763,0.00067669,0.000477506,0.0003420261,0.00001191663,0.001910034,0.000002378277],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978648,0.0001074183,0.0001117251,0.00005340064,0.000004335138,0.000009114364,0.0002935464,0.00001306591,0.001542743],"genre_scores_gemma":[0.9980393,0.0001327597,0.0005139474,0.0000195167,0.000005323288,0.0000131453,0.0005709415,0.000002160799,0.0007027851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.200963,"threshold_uncertainty_score":0.3995864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306110609515755,"score_gpt":0.2217820545437788,"score_spread":0.2087209484486212,"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."}}