{"id":"W2125836583","doi":"10.3390/rs70810607","title":"Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations","funders":"National Remote Sensing Centre","keywords":"Remote sensing; Environmental science; Lidar; Biomass (ecology); Estimation; Tropical forest; Geography; Geology; Ecology; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001780225,0.0003765424,0.0001962333,0.0007713039,0.0002403347,0.0004995079,0.0005636611,0.0002143726,0.0004040121],"category_scores_gemma":[0.000269683,0.0001647038,0.0004420782,0.0008955657,0.000198264,0.0002974487,0.0002847695,0.0001973061,0.0001408269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006026826,"about_ca_system_score_gemma":0.0004754204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06190939,"about_ca_topic_score_gemma":0.05643762,"domain_scores_codex":[0.9999202,0.00001247956,0.000005379151,0.00002434143,0.0000154887,0.00002203873],"domain_scores_gemma":[0.9998711,0.00004588582,0.00002605609,0.00001578884,0.00002980743,0.00001124479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000175172,0.0001917842,0.3421808,0.0001691336,0.0001348065,0.0005775465,0.0003045043,0.5712044,0.02059822,0.0007779639,0.0007609098,0.06292474],"study_design_scores_gemma":[0.000008316304,0.00004282094,0.1971355,0.00001463044,0.00004155821,0.0001460053,0.0003311114,0.7987077,0.002932149,0.0002582292,0.0003545607,0.00002741429],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949498,0.00004964106,0.003916586,0.00002399606,0.000003038193,0.000007602322,0.0002634762,0.000111573,0.0006741073],"genre_scores_gemma":[0.9973411,0.00003232563,0.002242996,0.000004588971,0.000001237242,0.000004172118,0.0002238494,0.00000719232,0.0001425455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06190939,"threshold_uncertainty_score":0.1230981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03914146155632042,"score_gpt":0.2835180965493181,"score_spread":0.2443766349929977,"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."}}