{"id":"W2518659878","doi":"10.7183/2326-3768.4.3.371","title":"Understanding Ancient Maya Agricultural Terrace Systems through Lidar and Hydrological Mapping","year":2016,"lang":"en","type":"article","venue":"Advances in Archaeological Practice","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Terrace (agriculture); Digital elevation model; Geology; Plateau (mathematics); Lidar; Archaeology; Drainage; Excavation; Drainage network; River terraces; Maya; Natural (archaeology); Physical geography; Hydrology (agriculture); Geography; Remote sensing; Drainage basin; Geomorphology; Fluvial; Structural basin; Cartography; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001313696,0.000216804,0.0002853576,0.00007922496,0.0003063497,0.00003612512,0.0002735641,0.0001696998,0.000404312],"category_scores_gemma":[0.003920452,0.0001046805,0.00004610659,0.0003703386,0.001224956,0.001946897,0.0001702526,0.0004963796,0.00009983514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005325263,"about_ca_system_score_gemma":0.00001963288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003313994,"about_ca_topic_score_gemma":0.000379924,"domain_scores_codex":[0.9969236,0.0008503431,0.0003606517,0.000626874,0.0004736113,0.000764891],"domain_scores_gemma":[0.99496,0.004454929,0.0001605578,0.0001582228,0.00004538647,0.000220967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002884236,0.0001511591,0.6129025,0.0001689637,0.00006702635,0.001329892,0.002666685,0.004781705,0.001821064,0.09185661,0.0001408796,0.2812292],"study_design_scores_gemma":[0.001500584,0.002592092,0.5890178,0.0002248766,0.00001429039,0.0009768475,0.005508528,0.001780145,0.00006606446,0.235834,0.1617312,0.0007536279],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5986879,0.02796864,0.2923571,0.02271798,0.0007472445,0.001440106,0.00004273177,0.0002809968,0.0557572],"genre_scores_gemma":[0.9790857,0.01104816,0.009160065,0.0004782723,0.0001134407,0.00001583814,0.000006622343,0.000002803327,0.00008904522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3803978,"threshold_uncertainty_score":0.469343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09968849633472522,"score_gpt":0.3034701137438217,"score_spread":0.2037816174090965,"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."}}