{"id":"W2326871339","doi":"10.5623/cig2011-044","title":"Integrating Socio-Economic Data For Integrated Land Management (ILM): Examples From The Humber River Basin, Western Newfoundland","year":2011,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Agentschap NL; Natural Resources Canada; University of Ottawa","keywords":"Geomatics; Geospatial analysis; Geography; Environmental resource management; Socioeconomic status; Land use; Environmental planning; Cartography; Data science; Computer science; Environmental science; Civil engineering; Population; Engineering; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003196586,0.0001498736,0.0001740453,0.00001014026,0.0002098726,0.00009709421,0.0008140447,0.00004853834,0.003149415],"category_scores_gemma":[0.00000910907,0.00008596379,0.00003923601,0.00003863882,0.0000419039,0.0003309241,0.0005031543,0.0000476127,0.001386768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006850527,"about_ca_system_score_gemma":0.000006074024,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03239937,"about_ca_topic_score_gemma":0.07074084,"domain_scores_codex":[0.9990065,0.00004755848,0.0002549787,0.0003409956,0.0001035633,0.0002464266],"domain_scores_gemma":[0.9989427,0.0001640229,0.0001056216,0.0007304507,0.00000403379,0.00005321852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002915599,0.000044141,0.9843687,0.00003506398,0.0001417053,0.000003770784,0.003463072,0.00002300687,0.000009893796,0.00009620532,0.006473942,0.005311331],"study_design_scores_gemma":[0.001115065,0.00006039006,0.8723735,0.0001599629,0.0002407651,0.000005302361,0.002925776,0.0204084,0.00002687916,0.007941744,0.09429619,0.0004460698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906824,0.00002648194,0.001413202,0.0002186263,0.0001753339,0.000410147,0.0003016548,0.00004321985,0.006728968],"genre_scores_gemma":[0.9901182,0.00002681968,0.00856474,0.0004384824,0.0001127782,0.00003751428,0.0004278085,0.00002089195,0.000252777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1119953,"threshold_uncertainty_score":0.9993908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288952569702065,"score_gpt":0.237939164381268,"score_spread":0.1950496386842474,"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."}}