{"id":"W1981730922","doi":"10.5751/es-05639-180132","title":"Applying Landscape Science to Natural Resource Management","year":2013,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Rural development and sustainability","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Natural resource management; Ecosystem management; Feature (linguistics); Natural (archaeology); Natural resource; Environmental resource management; Landscape ecology; Key (lock); Resource (disambiguation); Resource management (computing); Land management; Ecology; Land use; Computer science; Geography; Environmental science; Ecosystem; Archaeology","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":[0.0003241503,0.00006997697,0.00008137227,0.000005734901,0.0005738043,0.0000651156,0.0001643404,0.00004679212,0.0002002341],"category_scores_gemma":[0.00001590019,0.00002685362,0.0000361896,0.0002809177,0.0001448099,0.0001194467,0.0002067155,0.00007879606,0.0000475231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002851441,"about_ca_system_score_gemma":0.000005331823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001767062,"about_ca_topic_score_gemma":0.00002062588,"domain_scores_codex":[0.9992602,0.00001834369,0.00008609446,0.0002463307,0.00009824188,0.0002908016],"domain_scores_gemma":[0.9997565,0.00005988331,0.00001690698,0.00003225391,0.0000439919,0.00009045916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001571142,0.00009391121,0.4576553,0.00003640796,0.00004255678,0.000005033711,0.002235956,0.000003441717,0.02059559,0.004593201,0.1679893,0.3467336],"study_design_scores_gemma":[0.00005471825,0.00003313844,0.9584091,0.000001662361,0.000001986157,0.000001307131,0.003975489,0.00005471045,0.000078112,0.0008581128,0.03644421,0.00008743327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900824,0.00003172968,5.861846e-7,0.004249533,0.0000744123,0.0004592327,5.86338e-7,0.00003648282,0.005065019],"genre_scores_gemma":[0.9946632,0.00001477218,0.0004658783,0.002422649,0.000041278,0.0001426984,0.000004781406,2.706762e-7,0.00224442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5007538,"threshold_uncertainty_score":0.4413297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005671326684847671,"score_gpt":0.2011797862301673,"score_spread":0.1955084595453196,"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."}}