{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002150723,0.0006887285,0.0004652694,0.002228771,0.003947154,0.01098063,0.00104768,0.002299596,0.02872181],"category_scores_gemma":[0.006817888,0.0005491753,0.0006979805,0.002173444,0.008712665,0.00744272,0.003166944,0.00273407,0.007391743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004746603,"about_ca_system_score_gemma":0.004713824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006872309,"about_ca_topic_score_gemma":0.006212057,"domain_scores_codex":[0.9972388,0.0008128647,0.0001312419,0.0005480452,0.0009418516,0.0003272284],"domain_scores_gemma":[0.996852,0.001093888,0.0003827575,0.0003590895,0.000939604,0.0003726127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004176486,0.00002988799,0.00144424,0.0001557152,0.00001737785,0.0003417659,0.007169361,0.000371048,0.0006985329,0.9570802,0.00586631,0.02678379],"study_design_scores_gemma":[0.00003582731,0.00005526049,0.006177804,0.0004301022,0.00005441465,0.0009256911,0.01216847,0.001688443,0.00177659,0.6499168,0.326691,0.00007964435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04655712,0.008203344,0.05560759,0.01633587,0.0008578497,0.0001299321,0.0004160243,0.0002177959,0.8716745],"genre_scores_gemma":[0.8105219,0.007292112,0.04048203,0.001508968,0.0005594526,0.0003131727,0.000398213,0.0003201179,0.1386041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02872181,"threshold_uncertainty_score":0.096084,"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."}}