{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002228851,0.0001323645,0.0002338127,0.0002846022,0.0003033367,0.00014309,0.0005899016,0.0001298987,0.0005700129],"category_scores_gemma":[0.001796734,0.0001358542,0.0001091812,0.0003778679,0.0002329276,0.0007059562,0.0001400996,0.0001590921,0.00004671344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003293897,"about_ca_system_score_gemma":0.0002960271,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02410345,"about_ca_topic_score_gemma":0.01529309,"domain_scores_codex":[0.9974508,0.0001551382,0.0003292367,0.0003481399,0.001139583,0.0005771],"domain_scores_gemma":[0.9988434,0.0001927879,0.0001416882,0.0001870113,0.0004820763,0.0001530399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001447675,0.0007637921,0.9130508,0.0003292506,0.0001154484,0.00001160822,0.02282608,0.0002963271,0.004056833,0.007655761,0.008542566,0.04220673],"study_design_scores_gemma":[0.001756838,0.00009638244,0.9562017,0.0002074435,0.00001231831,3.263868e-7,0.00495436,0.002621097,0.002453711,0.03058731,0.0006959104,0.0004125965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548409,0.00007580638,0.0003734069,0.001855777,0.0003919002,0.001423316,0.00001765752,0.00005019189,0.04097104],"genre_scores_gemma":[0.9980878,0.000008157193,0.0007526767,0.00005003384,0.0005010324,0.0001484355,0.0000143099,0.00001757152,0.0004199732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0432469,"threshold_uncertainty_score":0.9823951,"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."}}