{"id":"W2087513702","doi":"10.1109/igarss.2013.6721332","title":"Forest change detection based on GNSS signal strength measurements","year":2013,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alzheimer Society Research Program","keywords":"GNSS applications; Remote sensing; SIGNAL (programming language); Satellite system; Computer science; Signal strength; Global Positioning System; Satellite; Satellite navigation; Environmental science; Telecommunications; Geology; Engineering; Antenna (radio)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000759836,0.00009372699,0.00005922087,0.0000249025,0.00009159649,0.00002644893,0.0000626668,0.00004892845,0.001187955],"category_scores_gemma":[0.000008493778,0.00006779929,0.00003471043,0.0001134901,0.00003555324,0.0001259734,0.00002256797,0.00006692541,0.001616057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009624301,"about_ca_system_score_gemma":0.000001811303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004375944,"about_ca_topic_score_gemma":0.006888625,"domain_scores_codex":[0.9992118,0.00002692198,0.00008077618,0.0001841607,0.0003139225,0.0001823689],"domain_scores_gemma":[0.999742,0.00001816975,0.00002723798,0.0001334869,0.00000626461,0.00007287069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001234951,0.0001109208,0.120041,0.000003300521,0.000006215535,0.000002096402,0.00008397571,0.0009335839,0.03558137,0.000001415285,0.001005133,0.8422187],"study_design_scores_gemma":[0.0002480083,0.0001450829,0.8982963,0.00001223224,0.000007106516,0.000001346314,0.00003350273,0.05478664,0.04528052,0.00007908535,0.0009562023,0.0001539799],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7845374,0.000003837047,0.001604302,0.0003325183,0.0002087287,0.0003518951,1.706683e-7,0.00008540798,0.2128758],"genre_scores_gemma":[0.9980002,4.727636e-7,0.00048771,0.001084582,0.00008739628,0.000004481448,0.000001648277,0.000009229923,0.0003242652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8420647,"threshold_uncertainty_score":0.9997251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470691338272661,"score_gpt":0.21376277760912,"score_spread":0.1790558642263934,"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."}}