{"id":"W4387250367","doi":"10.26848/rbgf.v16.5.p2741-2657","title":"Uso de Laser Scanner Terrestre para Estimativa de Biomassa Acima do Solo em Floresta Tropical Sazonalmente Seca","year":2023,"lang":"pt","type":"article","venue":"Revista Brasileira de Geografia Física","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Forestry; Biomass (ecology); Environmental science; Physics; Geography; Biology; Ecology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001084872,0.0008633352,0.0008320109,0.0002674438,0.0008030194,0.0005609657,0.001398343,0.0005977858,0.004560562],"category_scores_gemma":[0.0003542144,0.0008507575,0.0005224096,0.001392859,0.0009469106,0.0004492232,0.001379165,0.0008017473,0.006791363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130207,"about_ca_system_score_gemma":0.0002554808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000247802,"about_ca_topic_score_gemma":0.0004818569,"domain_scores_codex":[0.9935331,0.0007206728,0.0008986029,0.001438132,0.000764824,0.002644641],"domain_scores_gemma":[0.9970698,0.0003270906,0.0003661067,0.001268363,0.00002666503,0.0009419128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002273696,0.0006406496,0.845477,0.0004000539,0.0003612358,0.001618977,0.001119189,0.002341777,0.001000314,0.004070053,0.1388429,0.003900549],"study_design_scores_gemma":[0.001059358,0.0003916528,0.9380624,0.0003299334,0.0003333596,0.0001002529,0.0004594875,0.01316428,0.0002572679,0.0006016784,0.0443685,0.0008718579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917519,0.0003478999,0.002021857,0.002255494,0.0003970912,0.001110141,0.0002374582,0.0004314688,0.001446692],"genre_scores_gemma":[0.9883814,0.0002712555,0.001670566,0.001022764,0.0002961629,0.0002509782,0.0001846816,0.0001210118,0.007801149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09447436,"threshold_uncertainty_score":0.9993943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652771042141057,"score_gpt":0.2712678338085437,"score_spread":0.2547401233871331,"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."}}