{"id":"W1963788158","doi":"10.5539/jgg.v2n1p93","title":"A Typical Agricultural Areas of China -Dezhou City’s Land use Structure Changes Based on Past Decade Data","year":2010,"lang":"en","type":"article","venue":"Journal of Geography and Geology","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land use; China; Index (typography); Geography; Cultivated land; Agriculture; Land use, land-use change and forestry; Agricultural land; Scale (ratio); Physical geography; Environmental science; Agricultural economics; Cartography; Civil engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0001575038,0.000175145,0.0001277123,0.001351857,0.00030148,0.0002942283,0.0001461129,0.00008519331,0.0009620695],"category_scores_gemma":[0.0002188107,0.0001145063,0.000174879,0.002562928,0.0001492139,0.0001647086,0.0002081596,0.00007468688,0.0001023894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008559203,"about_ca_system_score_gemma":0.0005776539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0476383,"about_ca_topic_score_gemma":0.1165801,"domain_scores_codex":[0.999902,0.000009170623,0.00001135152,0.00003024689,0.00002310987,0.00002411596],"domain_scores_gemma":[0.9998442,0.00002211596,0.00004247003,0.00001532056,0.00004340021,0.00003255008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006283981,0.00001920965,0.9824249,0.00005536964,0.00005820829,0.0002662205,0.0004065142,0.001859909,0.002860268,0.0002444424,0.000689225,0.01105302],"study_design_scores_gemma":[0.000002077904,0.000009895462,0.9976314,0.000002045886,0.000009606151,0.00006998071,0.0001652596,0.0007864062,0.0002400363,0.000014426,0.001065468,0.000003284401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970656,0.00007744364,0.0001869466,0.00001474531,0.000001796958,0.000009281213,0.001915945,0.000009642269,0.0007186262],"genre_scores_gemma":[0.9962509,0.00008022715,0.0002249187,0.000004061155,0.000001231182,0.00001145623,0.002652246,0.000001168078,0.0007738849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0476383,"threshold_uncertainty_score":0.09472203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249409897259175,"score_gpt":0.2123322450030457,"score_spread":0.199838146030454,"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."}}