{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008756176,0.0002182605,0.0002243364,0.00008265795,0.0001971921,0.00003895656,0.000464402,0.0001116869,0.00008586433],"category_scores_gemma":[0.00009040365,0.0001273281,0.0000805742,0.0002874471,0.001650701,0.0006090316,0.0005392206,0.0005798702,0.00004042391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001797888,"about_ca_system_score_gemma":0.0000309628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002269536,"about_ca_topic_score_gemma":0.00008368439,"domain_scores_codex":[0.9965934,0.000433614,0.0003309419,0.0006084808,0.00137852,0.0006550391],"domain_scores_gemma":[0.9989754,0.0002071713,0.0001084334,0.0005930596,0.00000294495,0.0001129666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001342899,0.0003223093,0.1139738,0.00003486489,0.00001945806,0.00002085269,0.0008844745,0.121654,0.6878064,0.00002785099,0.001429778,0.07369197],"study_design_scores_gemma":[0.0005628912,0.00005808461,0.8991023,0.00003966983,0.000007364154,0.00002586475,0.0001512383,0.09330373,0.005925696,0.0003937065,0.0002067942,0.000222653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835693,0.00004020677,0.01251616,0.001616963,0.0000424489,0.0007455847,0.00001765857,0.00001470937,0.001436973],"genre_scores_gemma":[0.989752,0.00002111868,0.009651991,0.0001719984,0.00005585865,0.00001187871,0.00001376384,0.00002764799,0.0002937629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7851285,"threshold_uncertainty_score":0.6082076,"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."}}