{"id":"W2037238618","doi":"10.1007/s10818-011-9123-z","title":"Land-use changes, forest/soil conditions and carbon sequestration dynamics: A bio-economic model at watershed level in Nepal","year":2011,"lang":"en","type":"article","venue":"Journal of Bioeconomics","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Carbon sequestration; Present value; Economics; Net present value; Clearing; Agricultural economics; Watershed; Land use; Natural resource economics; Agriculture; Biomass (ecology); Environmental science; Geography; Ecology; Production (economics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002000762,0.00009547638,0.0001382796,0.0001118888,0.00006886989,0.00003426864,0.0001152796,0.00006240075,0.00009037586],"category_scores_gemma":[0.000003724566,0.00009014885,0.00003803418,0.00002469351,0.0001107121,0.0002284085,0.0001265465,0.00007263452,0.00001597425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006760941,"about_ca_system_score_gemma":0.00001738505,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002824336,"about_ca_topic_score_gemma":0.04280492,"domain_scores_codex":[0.9994112,0.00001953595,0.0002537321,0.0001333377,0.00004717458,0.0001350404],"domain_scores_gemma":[0.999594,0.00001325935,0.0002159322,0.00009451895,0.000006333129,0.00007593938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005713054,0.00003123799,0.9914256,0.000004747716,0.00001875952,0.000007257059,0.0007417668,0.007087701,0.0001697317,0.0000449775,0.00008832478,0.0003227736],"study_design_scores_gemma":[0.0006608577,0.00009059411,0.7790777,0.00000788933,0.00002929768,0.00001944122,0.000240852,0.2187242,0.0001350568,0.0006241609,0.0002594918,0.0001304354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983062,0.000009834217,0.0000657595,0.0005124851,0.00008819085,0.00009803577,0.00004662969,0.000004939122,0.0008679639],"genre_scores_gemma":[0.9986389,0.0001697553,0.0006265431,0.0001711624,0.00002701901,0.000001536162,0.00001953177,0.000006099699,0.0003394046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2123479,"threshold_uncertainty_score":0.9746614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04689673196274052,"score_gpt":0.2048848155022595,"score_spread":0.157988083539519,"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."}}