{"id":"W2481308876","doi":"10.1088/1748-9326/11/7/075004","title":"Estimation of aboveground net primary productivity in secondary tropical dry forests using the Carnegie–Ames–Stanford approach (CASA) model","year":2016,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Inter-American Institute for Global Change Research; National Science Foundation","keywords":"Primary production; Ecosystem; Tropical and subtropical dry broadleaf forests; Environmental science; Deforestation (computer science); Forestry; Photosynthetically active radiation; Productivity; Ecology; Carbon sequestration; Geography; Agroforestry; Biology; Botany","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":[0.0004948628,0.000388823,0.0002663552,0.0006627265,0.00029223,0.0003049169,0.0005336702,0.0002233738,0.000556021],"category_scores_gemma":[0.0007435951,0.0001967585,0.0008901126,0.0005310893,0.000194927,0.0002368892,0.0003910565,0.0002375805,0.0001127729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008127243,"about_ca_system_score_gemma":0.0009055065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08909302,"about_ca_topic_score_gemma":0.0985627,"domain_scores_codex":[0.9998388,0.00003777872,0.00001232967,0.00006557195,0.0000216522,0.00002379073],"domain_scores_gemma":[0.9997249,0.0001129337,0.00006245137,0.00002626941,0.00003982718,0.00003365627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002011784,0.0001106193,0.5101371,0.00007328395,0.0003398876,0.000205245,0.0001651209,0.4604154,0.006682502,0.00117303,0.0005073466,0.01998936],"study_design_scores_gemma":[0.00001176497,0.00003287858,0.1677745,0.000006143779,0.00003726466,0.00007447957,0.00006624488,0.8306516,0.0006222645,0.0003228266,0.000384561,0.00001556119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982915,0.00006844591,0.01554467,0.00002761927,0.000003859867,0.00002066429,0.0004942241,0.00006580759,0.0008595833],"genre_scores_gemma":[0.9919552,0.00003157214,0.007341896,0.000007361745,0.000002630253,0.00003163936,0.0004231113,0.000008868347,0.0001977651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08909302,"threshold_uncertainty_score":0.1771489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03032455385588524,"score_gpt":0.2718119155035602,"score_spread":0.241487361647675,"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."}}