{"id":"W2995817908","doi":"10.4236/gep.2019.712010","title":"Enhancing the Social and Natural Capital of Canadian Agro-Ecosystems through Incentive-Based “Alternative Land Use Services” (ALUS) Programs: Recurring Themes and Emerging Lessons","year":2019,"lang":"en","type":"article","venue":"Journal of Geoscience and Environment Protection","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Agriculture and Agri-Food Canada","keywords":"Incentive; Receipt; Incentive program; Business; Land use; Ecosystem services; Economics; Accounting; Ecosystem; 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":[0.0005355845,0.00008608093,0.0001718358,0.0001387091,0.0002252001,0.00007791727,0.00006957569,0.00003931225,0.00001567615],"category_scores_gemma":[0.000007569524,0.0000711933,0.00003289573,0.00007089899,0.0001018329,0.0007637053,0.00003476336,0.000129031,0.000003078792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286275,"about_ca_system_score_gemma":0.00001082861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02338497,"about_ca_topic_score_gemma":0.003736426,"domain_scores_codex":[0.9992754,0.0000236726,0.0003278157,0.0001733777,0.00005858828,0.000141148],"domain_scores_gemma":[0.9993814,0.00001720591,0.0004970998,0.00005685561,0.000006331603,0.00004109211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004224936,0.00006071718,0.9665762,0.0001226232,0.00006658315,0.000001442882,0.01315971,0.0005643589,0.004492294,0.001728236,0.000001341273,0.01318427],"study_design_scores_gemma":[0.0009341071,0.0003325213,0.9714309,0.0001483329,0.00002053826,0.00002422395,0.006225921,0.01493502,0.001522898,0.00193518,0.002261745,0.0002286335],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971717,0.0007169551,0.0009760558,0.0006634813,0.000162705,0.0002637173,0.000009731491,0.000001524991,0.00003411626],"genre_scores_gemma":[0.9988639,0.0007502401,0.0002442352,0.00004588164,0.00003850889,0.00000645995,0.000001506669,0.000005439996,0.00004386192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01964855,"threshold_uncertainty_score":0.9831184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0573542867272369,"score_gpt":0.2219360506445746,"score_spread":0.1645817639173378,"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."}}