{"id":"W2039556765","doi":"10.1109/lcnw.2014.6927707","title":"Predicting RF path loss in forests using satellite measurements of vegetation indices","year":2014,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Path (computing); Field (mathematics); Path loss; Satellite; Vegetation (pathology); Computer science; Environmental science; Wireless; Telecommunications; Geography; Mathematics; Engineering; Computer network; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003666813,0.00007398496,0.0001018043,0.0001215886,0.0000171955,0.0000110938,0.00004709984,0.00004104788,0.000006882139],"category_scores_gemma":[0.00003314611,0.00007231809,0.00002074912,0.0001015019,0.000007608702,0.0001313142,0.00001037226,0.00005610176,0.000003980958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003269844,"about_ca_system_score_gemma":0.000005900152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004306378,"about_ca_topic_score_gemma":0.0002225278,"domain_scores_codex":[0.999339,0.00002191938,0.000266537,0.00008819044,0.0001608133,0.0001234669],"domain_scores_gemma":[0.9997896,0.00001957558,0.00004514356,0.00007870784,0.00003860443,0.00002834796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004071298,0.00001368921,0.4787411,0.0002541124,0.00001762253,3.819574e-7,0.0008924985,0.3723667,0.1272916,0.00002010601,4.922771e-7,0.02039766],"study_design_scores_gemma":[0.0002564564,0.00001211331,0.02625454,0.0001388263,0.000006896873,7.364347e-7,0.00002059656,0.8249681,0.1480563,0.000201509,0.000005056193,0.00007889359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7369532,0.0001248927,0.2605972,0.00000162911,0.00007351334,0.00007046373,2.533188e-7,0.00004750115,0.00213131],"genre_scores_gemma":[0.9925081,0.00001317293,0.007416902,0.00001186715,0.00002588889,0.000002677185,0.00000342426,0.00001431377,0.00000367691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4526014,"threshold_uncertainty_score":0.2949046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04502698742815352,"score_gpt":0.2503081312864887,"score_spread":0.2052811438583352,"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."}}