{"id":"W4321792691","doi":"10.1016/j.ufug.2023.127878","title":"Estimating Settlement carbon stock and density using an inventory approach and quantifying their variation by land use and parcel size","year":2023,"lang":"en","type":"article","venue":"Urban forestry & urban greening","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Land use; Land cover; Carbon stock; Settlement (finance); Environmental science; Greenhouse gas; Carbon accounting; Forestry; Human settlement; Geography; Physical geography; Climate change; Ecology; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008352872,0.0004471735,0.0002642716,0.003864744,0.0004027167,0.0009659367,0.0006776452,0.0002053792,0.001361993],"category_scores_gemma":[0.001917548,0.0002510561,0.0004305411,0.003582317,0.0003063894,0.0007129316,0.0006507154,0.000196794,0.0003554527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008922524,"about_ca_system_score_gemma":0.0004223995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02807038,"about_ca_topic_score_gemma":0.05836066,"domain_scores_codex":[0.9994919,0.00009686612,0.00004324946,0.0001363454,0.0001724436,0.00005916193],"domain_scores_gemma":[0.9990465,0.0002427857,0.000259913,0.0001342883,0.000273267,0.00004320389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004275118,0.00005349645,0.9580714,0.00002646746,0.00009028202,0.00008389128,0.0004759831,0.008469105,0.001902129,0.0006716204,0.000412861,0.02969998],"study_design_scores_gemma":[0.000006259663,0.00006121768,0.9306521,0.0000170915,0.00005276041,0.0001873613,0.001288207,0.06355037,0.001817006,0.0007625147,0.00157979,0.0000252875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685371,0.00009833408,0.02436904,0.00002517329,0.00000596579,0.0001346054,0.002262847,0.000100163,0.004466726],"genre_scores_gemma":[0.9732068,0.00008855825,0.0232766,0.0000105389,0.000006134997,0.000168585,0.002250434,0.00001640045,0.0009757535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02807038,"threshold_uncertainty_score":0.05581403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05091966597558464,"score_gpt":0.2389593575896349,"score_spread":0.1880396916140503,"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."}}