{"id":"W2099720535","doi":"10.5751/es-02329-130140","title":"The Conservation Contributions of Conservation Easements: Analysis of the San Francisco Bay Area Protected Lands Spatial Database","year":2008,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Easement; Bay; Geography; Nature Conservation; Database; Protected area; Land use; Environmental resource management; Ecology; Archaeology; Environmental science; Political science; Computer science; Biology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005373503,0.00006265781,0.0002199822,0.00002765847,0.0003829398,0.000004731172,0.00008340172,0.00008624615,0.0001605272],"category_scores_gemma":[0.0001299657,0.00004993187,0.0001136178,0.0001881404,0.0003306878,0.0000835414,0.00004191199,0.00008015931,0.00000340043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005630469,"about_ca_system_score_gemma":0.00002570496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005359346,"about_ca_topic_score_gemma":0.001058064,"domain_scores_codex":[0.9992858,0.00003776556,0.0004112742,0.0001416349,0.0000252256,0.00009831996],"domain_scores_gemma":[0.9992025,0.0001425884,0.0004210413,0.0001851582,0.00003065693,0.00001800762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000927472,0.00004433059,0.9938546,0.000004856076,0.0003392383,4.603395e-8,0.0004577165,0.000119949,0.0001526148,0.004074489,0.000924527,0.00001831621],"study_design_scores_gemma":[0.0005219622,0.00003106942,0.9761085,0.00000222483,0.00007685029,4.672132e-7,0.0001391866,0.02132579,0.0001995706,0.0007162865,0.0008276321,0.00005048538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945663,0.000163815,0.003220535,0.001017579,0.00009759366,0.000253819,0.0004701038,0.000004463736,0.0002058262],"genre_scores_gemma":[0.9988034,0.0003404937,0.000124933,0.0003811191,0.00001237506,0.00003504764,0.0001801805,0.000003067936,0.000119345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02120585,"threshold_uncertainty_score":0.2945302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04240906523086876,"score_gpt":0.2155542919717121,"score_spread":0.1731452267408434,"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."}}