{"id":"W2885643887","doi":"10.1111/2041-210x.13025","title":"Understanding and assessing vegetation health by in situ species and remote‐sensing approaches","year":2018,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"ELUTIS Modelling and Consulting (Canada)","funders":"","keywords":"Vegetation (pathology); Trait; Resource (disambiguation); Computer science; Remote sensing; Ecosystem; In situ; Data science; Environmental resource management; Ecology; Data mining; Environmental science; Geography; 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":[0.002580374,0.0008294089,0.0005281669,0.004393642,0.0004120189,0.002024482,0.001171816,0.001070555,0.00129399],"category_scores_gemma":[0.00251617,0.0003316329,0.0007119625,0.002586824,0.0006600667,0.002807827,0.001748341,0.0006875818,0.0004236365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000714466,"about_ca_system_score_gemma":0.0004636587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004529363,"about_ca_topic_score_gemma":0.007178428,"domain_scores_codex":[0.9985881,0.0004895408,0.0001156806,0.000403567,0.0003196289,0.00008339234],"domain_scores_gemma":[0.9975672,0.0008458296,0.0006016712,0.0003597793,0.0005226419,0.0001029443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003052586,0.0005736529,0.2869172,0.002773779,0.0009249393,0.0004983132,0.004432946,0.05781077,0.136368,0.01889976,0.005173272,0.4853221],"study_design_scores_gemma":[0.00005443999,0.0006699079,0.4405021,0.001637606,0.0008940836,0.001211908,0.01081803,0.3750272,0.05673023,0.0555614,0.05645461,0.0004384907],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3151132,0.004834172,0.6458282,0.0009438817,0.0002586421,0.0005873788,0.005201905,0.001994683,0.02523794],"genre_scores_gemma":[0.7066831,0.001758299,0.2867436,0.0003349977,0.0001131412,0.0003250921,0.002195786,0.0001421321,0.001703996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004529363,"threshold_uncertainty_score":0.01364648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2171949127037651,"score_gpt":0.3729996597867778,"score_spread":0.1558047470830128,"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."}}