{"id":"W4313414664","doi":"10.3390/buildings13010041","title":"Feasibility of Planting Trees around Buildings as a Nature-Based Solution of Carbon Sequestration—An LCA Approach Using Two Case Studies","year":2022,"lang":"en","type":"article","venue":"Buildings","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Greenhouse gas; Life-cycle assessment; Carbon sequestration; Global warming; Environmental science; Carbon fibers; Natural gas; Environmental engineering; Carbon credit; Climate change; Engineering; Carbon dioxide; Waste management; Computer science; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001340584,0.0002149687,0.0003352076,0.00007638107,0.0003984315,0.0000189229,0.0002320087,0.00007795064,0.00007021046],"category_scores_gemma":[0.0001975854,0.0002176541,0.0001089921,0.0003581513,0.0005304334,0.0003410291,0.0003641291,0.0003293565,2.975441e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231635,"about_ca_system_score_gemma":0.00004990863,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007270992,"about_ca_topic_score_gemma":0.0001137834,"domain_scores_codex":[0.9978676,0.0002688674,0.0004591725,0.00050056,0.0005676618,0.0003361356],"domain_scores_gemma":[0.9990454,0.00009820113,0.0003647687,0.0003785809,0.00001940036,0.0000936256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002171138,0.0008244188,0.588351,0.0001382574,0.00003837629,0.00008598705,0.003142823,0.07430881,0.3320711,0.0003331992,0.00001353852,0.0004753974],"study_design_scores_gemma":[0.007068614,0.003403665,0.1270041,0.0001461176,0.000745692,0.003138575,0.09163346,0.4175723,0.3354352,0.0108187,0.0002955207,0.00273807],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982884,0.0001185501,0.0004028025,0.00003719347,0.00006287353,0.0005051477,0.00002336755,0.00003424255,0.0005274037],"genre_scores_gemma":[0.9911153,0.000002408877,0.008725963,0.00006235316,0.00002219538,0.00002239497,0.00001270105,0.00001840225,0.00001825437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4613468,"threshold_uncertainty_score":0.9993397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0435893244045196,"score_gpt":0.3346161932018959,"score_spread":0.2910268687973763,"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."}}