{"id":"W2022827579","doi":"10.1007/s11284-012-0976-y","title":"Monitoring vegetation recovery after China's May 2008 Wenchuan earthquake using Landsat TM time‐series data: a case study in Mao County","year":2012,"lang":"en","type":"article","venue":"Ecological Research","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Elevation (ballistics); Richter magnitude scale; Physical geography; Environmental science; Remote sensing; Scale (ratio); Geology; Hydrology (agriculture); Geography; Climate change; Cartography; Geotechnical engineering","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":[0.0003439055,0.0002547724,0.0002140304,0.001119212,0.0005434282,0.0003549322,0.0004609883,0.0004328967,0.0002447036],"category_scores_gemma":[0.0006222774,0.0001688323,0.0002777633,0.001273643,0.0003210054,0.0002147618,0.0003743461,0.0001746724,0.00004692508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00108682,"about_ca_system_score_gemma":0.0005343512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09922794,"about_ca_topic_score_gemma":0.1590563,"domain_scores_codex":[0.9998553,0.00002768766,0.00001211168,0.00002895455,0.00003493868,0.00004097281],"domain_scores_gemma":[0.9995079,0.00009680246,0.0001370006,0.00004871156,0.0001062223,0.000103395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001737171,0.0002124648,0.9748368,0.00004583548,0.00009645486,0.006708642,0.001775734,0.002512691,0.004901382,0.00006575399,0.0003413849,0.00832916],"study_design_scores_gemma":[0.000009683401,0.00008659685,0.9911738,0.000005093269,0.00003550475,0.0003834221,0.001644065,0.005806983,0.0005940313,0.0000175515,0.0002340919,0.000009251912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998355,0.00001303241,0.00003106465,0.00001031762,4.206913e-7,0.000004568109,0.00005617577,0.000002071286,0.00004666858],"genre_scores_gemma":[0.9994882,0.00003097457,0.0001966479,0.000005362478,0.000002347898,0.000006678512,0.0001734037,9.260129e-7,0.00009536891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09922794,"threshold_uncertainty_score":0.1973007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09897093624286832,"score_gpt":0.3693888888017829,"score_spread":0.2704179525589147,"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."}}