{"id":"W4387821381","doi":"10.1038/s41598-023-44875-0","title":"Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Max-Planck-Gesellschaft","keywords":"Lidar; Documentation; Population; Bayesian probability; Regression; Geography; Archaeology; Artificial intelligence; Machine learning; Computer science; Physical geography; Remote sensing; Cartography; Statistics; Mathematics","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":[0.003708375,0.0007923435,0.0008419264,0.0009809644,0.0003634769,0.001034697,0.001138947,0.0009911456,0.001021371],"category_scores_gemma":[0.01033194,0.0005919382,0.0006053647,0.000786222,0.0005932893,0.001159674,0.0008422406,0.001622436,0.0004760614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009479252,"about_ca_system_score_gemma":0.0009621865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01922336,"about_ca_topic_score_gemma":0.01240905,"domain_scores_codex":[0.9992311,0.0004193128,0.00003795417,0.0001553705,0.0000905736,0.00006564688],"domain_scores_gemma":[0.9954437,0.003516302,0.0003679848,0.0001408975,0.000467883,0.00006325879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009119141,0.0001031474,0.007870002,0.00005735858,0.00007376325,0.00006826092,0.00008304894,0.9204665,0.001074957,0.005346066,0.0005558703,0.06420983],"study_design_scores_gemma":[0.000001965172,0.000005754122,0.0003351332,0.000003667872,0.000002940485,0.00000362936,0.000004633883,0.9986874,0.0001421736,0.0007017066,0.000107391,0.000003644385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1552167,0.001021508,0.8399328,0.0008257141,0.00005479397,0.00007627236,0.0001487672,0.0007531478,0.00197022],"genre_scores_gemma":[0.847698,0.0006868307,0.1474626,0.0001633058,0.0001187859,0.0001290472,0.000342651,0.0001138962,0.003284915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01922336,"threshold_uncertainty_score":0.03822297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0362213024637873,"score_gpt":0.2870639471151875,"score_spread":0.2508426446514002,"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."}}