{"id":"W4241880919","doi":"10.35735/tig.2019.14.76.005","title":"ЗЕМЕЛЬНЫЕ РЕСУРСЫ ПРИБРЕЖНЫХ РАЙОНОВ ТИХООКЕАНСКОЙ РОССИИ (ТР): МЕЛКОМАСШТАБНАЯ ТИПОЛОГИЯ","year":2019,"lang":"ru","type":"article","venue":"","topic":"Environmental Sustainability and Technology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land cover; Land use; Environmental resource management; Geography; Natural resource; China; State of the Environment; Scale (ratio); Environmental protection; Environmental planning; Environmental science; Political science; Cartography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008674213,0.001039147,0.0009972058,0.0002089388,0.0004848507,0.0001596621,0.001870545,0.001180751,0.2222675],"category_scores_gemma":[0.0001162931,0.001010113,0.000517097,0.0009053805,0.002037386,0.0009411557,0.002439463,0.001288853,0.09272252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905517,"about_ca_system_score_gemma":0.00007598853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002979077,"about_ca_topic_score_gemma":0.0005243515,"domain_scores_codex":[0.9928817,0.0002760464,0.001124541,0.002303014,0.001210446,0.002204234],"domain_scores_gemma":[0.9961649,0.0002550326,0.0003568254,0.002602686,0.00001914032,0.0006014187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003318458,0.003326595,0.8240412,0.0005226189,0.0003174054,0.0002728674,0.001207249,0.003464777,0.02300962,0.02088995,0.01475287,0.107863],"study_design_scores_gemma":[0.005151053,0.003100049,0.3270818,0.0001758818,0.0003308717,0.0002244123,0.009186783,0.002984593,0.03752187,0.02620084,0.5835428,0.004499095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8263349,0.0007791125,0.0003208683,0.004829647,0.001075504,0.001982425,0.00005812811,0.0004735694,0.1641459],"genre_scores_gemma":[0.9027939,0.0005078858,0.001274495,0.001838907,0.0001273876,0.00006698611,0.00003507806,0.000116025,0.09323932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5687899,"threshold_uncertainty_score":0.9992349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001949649721008584,"score_gpt":0.1736142937720492,"score_spread":0.1716646440510406,"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."}}