{"id":"W4220935876","doi":"10.1016/j.jnc.2022.126159","title":"Current capacity, bottlenecks, and future projections for offsetting habitat loss using Mitigation and Conservation banking in the United States","year":2022,"lang":"en","type":"article","venue":"Journal for Nature Conservation","topic":"Environmental Conservation and Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Habitat; Habitat conservation; Environmental science; Current (fluid); Environmental resource management; Habitat destruction; Natural resource economics; Business; Geography; Ecology; Geology; Economics; Oceanography; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001941042,0.0004109655,0.0003849715,0.001493919,0.0006942039,0.001492133,0.001670746,0.0009862004,0.005442769],"category_scores_gemma":[0.003694638,0.0003722353,0.0008072932,0.001711148,0.0006468981,0.003399986,0.001172015,0.001333526,0.000436951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004277922,"about_ca_system_score_gemma":0.004516552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1376716,"about_ca_topic_score_gemma":0.1910911,"domain_scores_codex":[0.9993278,0.0001637651,0.00003844376,0.00009034528,0.0001212178,0.0002583914],"domain_scores_gemma":[0.9976429,0.000415763,0.0004747084,0.00007071655,0.0007685193,0.0006272645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006721488,0.0007095026,0.6035295,0.0003604156,0.0003789896,0.0004405624,0.0007354701,0.227195,0.001161398,0.03339982,0.04861395,0.0828033],"study_design_scores_gemma":[0.0001057797,0.000336673,0.5429271,0.0006094448,0.0004598431,0.0005329799,0.007637716,0.3935545,0.001327394,0.02787391,0.0244576,0.0001770778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582161,0.002116516,0.006114164,0.01081406,0.000166548,0.00006408144,0.01173561,0.0002782408,0.01049469],"genre_scores_gemma":[0.9941893,0.0007080112,0.001601342,0.0002571326,0.00001819394,0.000049479,0.001989202,0.0000118908,0.001175424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1376716,"threshold_uncertainty_score":0.2737406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246063465215949,"score_gpt":0.2802344913673572,"score_spread":0.2556281448457623,"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."}}