{"id":"W2047516284","doi":"10.3390/rs5010042","title":"Using InSAR Coherence to Map Stand Age in a Boreal Forest","year":2012,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Interferometric synthetic aperture radar; Coherence (philosophical gambling strategy); Synthetic aperture radar; Environmental science; Taiga; Baseline (sea); Mean squared error; Scale (ratio); Radar; Geography; Cartography; Mathematics; Geology; Statistics; Computer science; Forestry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001374918,0.0001239054,0.0001467651,0.0001030365,0.00003809331,0.00002247395,0.00005903433,0.00007639329,0.000002289113],"category_scores_gemma":[0.00001828498,0.000126275,0.00002556481,0.0002071344,0.00001972885,0.00006812932,0.00002657347,0.000118827,0.00001121655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001474567,"about_ca_system_score_gemma":0.00001119806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003235057,"about_ca_topic_score_gemma":0.000153309,"domain_scores_codex":[0.9993055,0.00001429248,0.0001622999,0.0001159486,0.00009718102,0.0003048168],"domain_scores_gemma":[0.9996033,0.00004107734,0.00001727746,0.0002298089,0.0000165186,0.00009196876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003717832,0.000008581495,0.0002851444,0.00003707008,0.000007771006,0.00001486388,0.0006769746,0.00009756097,0.009751049,0.0001972072,0.0001836921,0.9887364],"study_design_scores_gemma":[0.0003256354,0.00002642168,0.006906641,0.0006974103,0.00002998349,0.0001498957,0.0002889754,0.4935859,0.04485909,0.001701231,0.4506601,0.0007686784],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3003634,0.0002458578,0.6927069,0.00004087405,0.00007706411,0.0001946443,0.000001583701,0.000206317,0.006163321],"genre_scores_gemma":[0.54298,0.000007375187,0.4568904,0.00002622742,0.00006501003,2.98111e-8,0.00000135269,0.00002089031,0.000008674922],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9879677,"threshold_uncertainty_score":0.5149347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609825996203642,"score_gpt":0.266208524164546,"score_spread":0.2401102642025096,"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."}}