{"id":"W4298110301","doi":"10.3390/su141912423","title":"Development of an Urban Turfgrass and Tree Carbon Calculator for Northern Temperate Climates","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Turfgrass Adaptation and Management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Landscape Alberta Nursery Trades Association; University of Guelph","funders":"Texas Tech University","keywords":"Carbon sequestration; Calculator; Carbon fibers; Temperate climate; Environmental science; Urban ecosystem; Carbon flux; Agroforestry; Ecosystem; Natural resource economics; Ecology; Urban planning; Computer science; Carbon dioxide; Biology; Economics","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":[0.0004299605,0.0000957515,0.0001188698,0.00002552862,0.0002461132,0.0000124234,0.0001214203,0.0000169898,0.00009741727],"category_scores_gemma":[0.00005167563,0.00009082058,0.00002927105,0.0001187887,0.00009275426,0.00006880056,0.0002661877,0.00004874468,4.492886e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007606353,"about_ca_system_score_gemma":0.00006235988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003784876,"about_ca_topic_score_gemma":0.001771924,"domain_scores_codex":[0.9990125,0.00006301427,0.0002196957,0.0002937185,0.00020881,0.0002022866],"domain_scores_gemma":[0.9995986,0.00002043659,0.0000713578,0.0002030399,0.00003396437,0.00007258541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001955516,0.0005880253,0.9140697,0.0002245386,0.00003176846,0.000003832261,0.01371612,0.004620351,0.003184828,0.001512559,0.000111919,0.0617408],"study_design_scores_gemma":[0.0007893285,0.0002279694,0.9301637,0.000001322276,0.00002126735,9.817182e-7,0.01828278,0.007434411,0.001469864,0.002264489,0.03907264,0.0002712208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984881,0.00001377971,0.0001182205,0.00024878,0.00003839349,0.0007030679,0.00000849095,0.00002829989,0.0003528251],"genre_scores_gemma":[0.9973641,4.598251e-7,0.002030763,0.00004519699,0.000005184781,0.0002338748,0.00001626132,0.000009362284,0.0002948128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06146958,"threshold_uncertainty_score":0.3703556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007536890236395406,"score_gpt":0.234047704674527,"score_spread":0.2265108144381316,"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."}}