{"id":"W2125700521","doi":"10.1109/igarss.2007.4423812","title":"Utilization of neural networks for the estimation of aboveground forest biomass from Ikonos satellite image and multi-source geo-scientific data","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"North-West University; University of Maryland Center for Environmental Science; New Brunswick Innovation Foundation; Université de Moncton","keywords":"Biomass (ecology); Mean squared error; Residual; Artificial neural network; Estimation; Satellite; Regression; Statistics; Mathematics; Environmental science; Remote sensing; Computer science; Agronomy; Geography; Artificial intelligence; Algorithm; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007064373,0.0006662415,0.0002480169,0.0009288273,0.0001566842,0.0004225633,0.0003661524,0.0003634322,0.0004838313],"category_scores_gemma":[0.002649893,0.0002182404,0.0002422656,0.0006230397,0.0001647503,0.0008437072,0.000294818,0.0002980255,0.0001844601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003679372,"about_ca_system_score_gemma":0.0002780999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005045584,"about_ca_topic_score_gemma":0.006950296,"domain_scores_codex":[0.999756,0.00007417568,0.00001751058,0.0000475243,0.00008521381,0.00001954384],"domain_scores_gemma":[0.999392,0.000354437,0.00007712985,0.0000331328,0.0001302923,0.00001297009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001800793,0.00009386796,0.01142854,0.0001535346,0.0001510811,0.0001209475,0.0000742333,0.53385,0.01846908,0.001599629,0.0004101369,0.4334689],"study_design_scores_gemma":[0.000004569487,0.00002322038,0.003286917,0.00001032656,0.00001808017,0.00001966882,0.00001773008,0.9918637,0.003646717,0.0008988939,0.0002015277,0.00000860452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.182631,0.000610183,0.8139321,0.000125418,0.0000325822,0.0000349627,0.0001391664,0.0005170088,0.001977622],"genre_scores_gemma":[0.787443,0.0003280101,0.2104197,0.00003371658,0.00002797912,0.00008533952,0.0002373455,0.00002925001,0.001395733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005045584,"threshold_uncertainty_score":0.01003247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04601448953612106,"score_gpt":0.2932480160834008,"score_spread":0.2472335265472798,"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."}}