{"id":"W4400462668","doi":"10.5558/tfc2024-019","title":"Estimating leakage rates for Ontario forest offset projects","year":2024,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; Ministry of Natural Resources and Forestry","funders":"","keywords":"Leakage (economics); Offset (computer science); Environmental science; Climate change; Percentile; Natural resource economics; Economics; Econometrics; Statistics; Mathematics; Computer science; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002171498,0.0003213474,0.0002351564,0.002887313,0.0005776982,0.0008368216,0.000405063,0.0003579096,0.001087351],"category_scores_gemma":[0.01055395,0.0002135578,0.0006142907,0.004322531,0.0005006662,0.0009281102,0.0007749028,0.0002999728,0.0001229525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01458098,"about_ca_system_score_gemma":0.00601899,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8468905,"about_ca_topic_score_gemma":0.8829381,"domain_scores_codex":[0.9986934,0.0002007026,0.0001245233,0.0001375387,0.0006562429,0.0001876379],"domain_scores_gemma":[0.9947546,0.001548512,0.001479506,0.0002697208,0.001850661,0.00009708346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002726059,0.0000390847,0.8056825,0.0002730865,0.0002155123,0.0003563105,0.001971712,0.1333816,0.001977445,0.005355915,0.002447393,0.04802682],"study_design_scores_gemma":[0.00002431404,0.00007310046,0.9020473,0.00008689558,0.00009809874,0.0001862139,0.001685185,0.08038925,0.002080433,0.002844959,0.01040768,0.00007669049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802876,0.000484389,0.00645675,0.000170445,0.000004190546,0.00007811747,0.00539522,0.0000443308,0.007079037],"genre_scores_gemma":[0.9912696,0.0004001559,0.003260828,0.00001273588,0.000002903531,0.00004270619,0.003067375,0.00001345065,0.001930281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1531095,"threshold_uncertainty_score":0.3080223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0903808993339506,"score_gpt":0.2559857512810379,"score_spread":0.1656048519470873,"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."}}