{"id":"W2349962284","doi":"","title":"The Formation Mechanism and Countermeasures to the Urban Spatial Structure Adjustment of Changchun","year":2008,"lang":"en","type":"article","venue":"Xiandai chengshi yanjiu","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Urban spatial structure; Urban planning; Urban structure; Environmental planning; Mode (computer interface); Urban space; Geography; Mechanism (biology); Urban density; Economic geography; Space (punctuation); Civil engineering; Transport engineering; Computer science; Engineering","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":[0.000209248,0.000104022,0.000112344,0.0000284855,0.0004451512,0.00003985377,0.0001403341,0.00004870498,0.00007340743],"category_scores_gemma":[0.00004133582,0.0000494975,0.00003289227,0.00008446734,0.00006399537,0.00007928118,0.00001118321,0.0001013436,0.0000149102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003749389,"about_ca_system_score_gemma":0.00002238176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274928,"about_ca_topic_score_gemma":0.004228144,"domain_scores_codex":[0.9991896,0.00006353315,0.0001554158,0.0001217986,0.00027913,0.0001905376],"domain_scores_gemma":[0.999538,0.00008081066,0.00007292042,0.0001864408,0.00004763111,0.00007416948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008715162,0.00004943575,0.05042652,0.0001582986,0.0002145604,0.00003042657,0.06101719,0.001900703,0.001820244,0.001135435,0.05420078,0.8281749],"study_design_scores_gemma":[0.0008756096,0.0005025337,0.8717062,0.00007544286,0.00005278965,0.0002217693,0.001159888,0.02439318,0.006482327,0.001506719,0.09267014,0.0003533261],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962214,0.0008461772,0.0002584657,0.0009640706,0.0004461315,0.0002412467,0.00009278596,0.00001877837,0.0009109305],"genre_scores_gemma":[0.998865,0.0002393855,0.00008218699,0.0003422418,0.000256404,2.514253e-7,0.00003227166,0.000002905752,0.000179359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8278216,"threshold_uncertainty_score":0.3423788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185105674865768,"score_gpt":0.1799348186321851,"score_spread":0.1680837618835274,"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."}}