{"id":"W2758174452","doi":"","title":"Effects of Drought on Carbon Sequestration of Tropical Dry Forest","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Tropical and subtropical dry broadleaf forests; Carbon sequestration; Tropical forest; Environmental science; Tropics; Agroforestry; Carbon sink; Forestry; Carbon fibers; Climate change; Geography; Carbon dioxide; Mathematics; Ecology; Biology","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":[0.000595236,0.0002499135,0.0002392093,0.0003030404,0.0003959789,0.0005324495,0.0002187197,0.0004724888,0.002366033],"category_scores_gemma":[0.00118688,0.0001213031,0.0003082264,0.0004180406,0.0004529268,0.0005465744,0.0005738652,0.0003514998,0.0001166481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009126972,"about_ca_system_score_gemma":0.000546113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01961484,"about_ca_topic_score_gemma":0.02467947,"domain_scores_codex":[0.9998388,0.0000409769,0.00001482856,0.00001901834,0.00001476544,0.00007154982],"domain_scores_gemma":[0.9994181,0.0001867055,0.0001350962,0.0000281269,0.00008100714,0.0001509836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005812076,0.0005254946,0.8880993,0.0003894763,0.0006434013,0.001007967,0.0004690792,0.03302391,0.04013274,0.002648213,0.003365779,0.02388243],"study_design_scores_gemma":[0.00007349707,0.0002226505,0.9855344,0.00001607917,0.0001614277,0.00008868383,0.0003933368,0.009695437,0.002137184,0.0006386128,0.00102209,0.0000164847],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998401,0.0001610143,0.00006007141,0.0002676169,0.00001931303,0.000002729272,0.0003939812,0.000006370473,0.0006879157],"genre_scores_gemma":[0.9995164,0.0001175213,0.00003401084,0.00003780246,0.00001454203,0.000002462622,0.0001524919,0.000002285148,0.0001226482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01961484,"threshold_uncertainty_score":0.03900135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009924064407896253,"score_gpt":0.228467873187935,"score_spread":0.2185438087800387,"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."}}