{"id":"W2606812312","doi":"10.5751/es-09148-220207","title":"Locating financial incentives among diverse motivations for long-term private land conservation","year":2017,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; University of Queensland; RMIT University; Imperial College London; Centre of Excellence for Environmental Decisions, Australian Research Council","keywords":"Incentive; Term (time); Knight; Nature Conservation; Business; Wildlife conservation; Finance; Ecology; Environmental resource management; Economics; Habitat; Biology; Market economy","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":[0.0001338939,0.00005074387,0.0000581329,0.000004477407,0.0008917221,0.00003489484,0.00009944939,0.00006111046,0.0001802795],"category_scores_gemma":[0.00008434529,0.00004874976,0.00003309488,0.00001763192,0.0004151638,0.0002428758,0.0001664968,0.00004466363,0.00002581996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002320385,"about_ca_system_score_gemma":0.000004563697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001233846,"about_ca_topic_score_gemma":0.0006980047,"domain_scores_codex":[0.9996478,0.00001038082,0.00006448937,0.0001198733,0.00003135369,0.0001260966],"domain_scores_gemma":[0.9997494,0.00004143432,0.00008490946,0.00009719085,0.000004315772,0.00002269832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003176777,0.00001483761,0.9926515,0.00001011869,0.000007109684,2.460532e-7,0.0006360413,0.00001112709,0.00001590203,0.0009016547,0.005216182,0.0005320826],"study_design_scores_gemma":[0.0003428068,0.00001920666,0.9968655,0.000003176815,0.00001117831,1.13791e-7,0.00001448894,0.001171948,0.00001510099,0.0005282083,0.0009716611,0.0000566793],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976282,0.00000225896,0.0002155002,0.0005385231,0.00009377116,0.0001995499,0.000004095562,0.00001127007,0.001306838],"genre_scores_gemma":[0.9970694,0.00001984314,0.0004208688,0.0003320767,0.00005269715,0.00002185057,0.00001317176,0.000002813157,0.002067336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004244521,"threshold_uncertainty_score":0.6858495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542695320009872,"score_gpt":0.2548920552975161,"score_spread":0.2394651020974174,"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."}}