{"id":"W2056254247","doi":"10.1080/01431160210154056","title":"Monitoring secondary tropical forests using space-borne data: Implications for Central America","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Carbon sequestration; Environmental science; Carbon sink; Amazon rainforest; Biomass (ecology); Radar; Stratification (seeds); Geography; Climate change; Ecology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001659219,0.0001342275,0.0001817727,0.00008719691,0.0001616218,0.0001002342,0.0004236623,0.00005814966,0.00002526709],"category_scores_gemma":[0.0003009166,0.0001293578,0.0001176452,0.000155393,0.0001163636,0.0003296652,0.000101186,0.0002261527,0.000008970704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003469619,"about_ca_system_score_gemma":0.00009340564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001349217,"about_ca_topic_score_gemma":0.00003415852,"domain_scores_codex":[0.9986395,0.00005523782,0.0004445404,0.0002547185,0.000331197,0.0002747564],"domain_scores_gemma":[0.9988351,0.0001344362,0.0003920557,0.0003450916,0.0001391326,0.000154135],"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":[0.00006552514,0.00006307513,0.008626108,0.000004937755,0.0001578992,0.00002642197,0.0003221155,0.006131266,0.06463291,0.0001965213,0.001524194,0.918249],"study_design_scores_gemma":[0.00301017,0.0001943315,0.3716228,0.0003885054,0.0002946846,0.005763386,0.0007357429,0.204114,0.02763747,0.01598408,0.3693277,0.0009271625],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3368586,0.00007284763,0.6583425,0.00194428,0.001032391,0.0001293688,0.00002018674,0.00001664523,0.001583139],"genre_scores_gemma":[0.6377937,0.00003256135,0.3616025,0.00006828524,0.0004424886,1.619931e-8,0.000006561341,0.00001702219,0.00003686513],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9173219,"threshold_uncertainty_score":0.5275058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0368091106949423,"score_gpt":0.3179247856784023,"score_spread":0.28111567498346,"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."}}