{"id":"W4401024781","doi":"10.3390/land13081144","title":"Per Capita Land Use through Time and Space: A New Database for (Pre)Historic Land-Use Reconstructions","year":2024,"lang":"en","type":"article","venue":"Land","topic":"Archaeology and ancient environmental studies","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Akademie der Naturwissenschaften; Chinese Academy of Sciences","keywords":"Land use; Per capita; Land cover; Database; Land use, land-use change and forestry; Range (aeronautics); Geography; Environmental resource management; Climate change; Perspective (graphical); Computer science; Environmental science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114655,0.0003872485,0.0006411768,0.006086049,0.0004617227,0.001594821,0.0008811195,0.0005931194,0.006956258],"category_scores_gemma":[0.003914199,0.0004689182,0.000421961,0.008948538,0.0003040325,0.001653571,0.001383571,0.0007668469,0.004394414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007378781,"about_ca_system_score_gemma":0.001174392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0136385,"about_ca_topic_score_gemma":0.02504155,"domain_scores_codex":[0.9993467,0.0000755896,0.0001642377,0.0001863103,0.0001763436,0.00005087421],"domain_scores_gemma":[0.9971408,0.0004927448,0.0004631042,0.000842551,0.0007981165,0.0002627471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008667009,0.0003188647,0.4489239,0.001457549,0.0004432811,0.0008319856,0.003850604,0.01409277,0.01100582,0.01057709,0.118542,0.3890895],"study_design_scores_gemma":[0.00008637121,0.00008021552,0.5072706,0.000265312,0.0002061307,0.000931895,0.001335239,0.01267927,0.007228253,0.003389849,0.4663845,0.0001423089],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1891707,0.001017016,0.04043134,0.0003426219,0.0001028608,0.0002411368,0.7524698,0.003098052,0.01312635],"genre_scores_gemma":[0.1700328,0.000797438,0.07396021,0.00008146578,0.00005108764,0.0006453531,0.7508797,0.0006320348,0.002920017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0136385,"threshold_uncertainty_score":0.02711821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187696404241112,"score_gpt":0.2212081714040335,"score_spread":0.1993312073616224,"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."}}