{"id":"W2365771718","doi":"","title":"MEASUREMENTS FOR SPATIAL ACCESSIBILITY OF NATIONAL FOREST PARKS IN CHINA","year":2013,"lang":"en","type":"article","venue":"Changjiang liuyu ziyuan yu huanjing","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Geography; Raster data; Tourism; Beijing; China; Prosperity; Spatial analysis; Environmental resource management; Sustainable development; Distribution (mathematics); National forest; Ecotourism; Common spatial pattern; Raster graphics; Ecology; Remote sensing; Forestry; Environmental science; Computer science; Economic growth","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":[0.0003893536,0.0001892468,0.0001254937,0.001670822,0.000338359,0.0003276555,0.0002091541,0.0001053895,0.001179532],"category_scores_gemma":[0.00163264,0.00008045788,0.0002459391,0.002296769,0.0002050398,0.0004228356,0.0004244955,0.00008707333,0.0001098734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005918338,"about_ca_system_score_gemma":0.0004568033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02774224,"about_ca_topic_score_gemma":0.03728838,"domain_scores_codex":[0.9996038,0.0000499019,0.00007855697,0.0000876683,0.0001367747,0.00004336111],"domain_scores_gemma":[0.9988422,0.0002096663,0.0003265552,0.0001119019,0.0003746274,0.0001350912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006200125,0.00002043643,0.9747798,0.00007016188,0.00004078107,0.0001087213,0.0008723668,0.002655873,0.001732717,0.0004105322,0.0003975767,0.01884902],"study_design_scores_gemma":[0.000001593935,0.00001994841,0.9968928,0.000004053542,0.000009577569,0.00005995825,0.0004243763,0.001665053,0.0003051221,0.00008721511,0.0005236613,0.000006612182],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970233,0.00005050427,0.0005516326,0.000009645226,0.000001731269,0.00001170376,0.0008832423,0.00002033292,0.001447797],"genre_scores_gemma":[0.9986333,0.00002773062,0.000448646,0.000002001241,0.000001217351,0.00001620477,0.0006231623,0.000001362258,0.0002464819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02774224,"threshold_uncertainty_score":0.05516154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183798705191147,"score_gpt":0.3881294041526247,"score_spread":0.26974953363351,"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."}}