{"id":"W2523115478","doi":"10.3390/f7090208","title":"Urban Forest Indicators for Planning and Designing Future Forests","year":2016,"lang":"en","type":"article","venue":"Forests","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Delphi method; Urban forestry; Urban forest; Environmental resource management; Environmental planning; Delphi; Business; Geography; Performance indicator; Forestry; Environmental science; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002221558,0.0001595325,0.0001524413,0.00007213477,0.0002395224,0.00002584022,0.000143133,0.00011342,0.0001192716],"category_scores_gemma":[0.00003297356,0.0001066271,0.00004476122,0.0001379352,0.0001426484,0.0002599359,0.0001004818,0.00007299103,0.00006638381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009015804,"about_ca_system_score_gemma":0.00001770588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000647353,"about_ca_topic_score_gemma":0.01010414,"domain_scores_codex":[0.998802,0.00001894334,0.0001677537,0.0003474564,0.0001894799,0.0004743597],"domain_scores_gemma":[0.9993437,0.0001126557,0.00009960843,0.0002073686,0.000004573229,0.0002320758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003052013,0.00001145997,0.9437342,0.00001242563,0.000005769563,0.000007194805,0.0003672837,0.000009684142,0.0001690276,0.0004168008,0.04999503,0.005240647],"study_design_scores_gemma":[0.0004787496,0.0001888035,0.9505004,0.00004829587,0.00001077857,0.00001046615,0.00004315036,0.00006980998,0.0001300157,0.002080213,0.04626402,0.0001753191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856808,0.0002818285,0.01183228,0.0009483792,0.0001844049,0.0004897916,0.00001247758,0.00007017382,0.0004998459],"genre_scores_gemma":[0.9963453,0.00001006996,0.00211408,0.0002016999,0.0003339873,0.0000676549,0.000005564,0.00002965589,0.000892015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01066446,"threshold_uncertainty_score":0.563835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415370990312397,"score_gpt":0.2536925838299596,"score_spread":0.2395388739268356,"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."}}