{"id":"W2990299715","doi":"10.1007/s11355-019-00402-w","title":"High uncertainties detected in the wetlands distribution of the Qinghai–Tibet Plateau based on multisource data","year":2019,"lang":"en","type":"article","venue":"Landscape and Ecological Engineering","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Wetland; Consistency (knowledge bases); Plateau (mathematics); Environmental science; Distribution (mathematics); Spatial distribution; Remote sensing; Pixel; Hydrology (agriculture); Physical geography; Computer science; Geography; Ecology; Geology; Mathematics","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":[0.002415146,0.0002775085,0.0002853535,0.002308501,0.0004663682,0.001169515,0.0003812963,0.0002794208,0.000556768],"category_scores_gemma":[0.003453642,0.0001575645,0.0003585053,0.002077225,0.0003973809,0.0006730147,0.0007073413,0.0001786632,0.00008851132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466189,"about_ca_system_score_gemma":0.0005775388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03656092,"about_ca_topic_score_gemma":0.05135811,"domain_scores_codex":[0.9989711,0.0002389236,0.0001087884,0.0003033192,0.0002752096,0.0001025605],"domain_scores_gemma":[0.9970673,0.001320386,0.0004262122,0.000372138,0.0007003121,0.0001136699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001559009,0.00003753042,0.9435239,0.0001187095,0.0003221745,0.0002477114,0.001148635,0.009699905,0.01203291,0.0006941894,0.0004869346,0.03153144],"study_design_scores_gemma":[0.000005257631,0.00001128502,0.9878073,0.00002112002,0.00005508566,0.0000469329,0.0004418642,0.009876635,0.0006623963,0.0002770912,0.0007788537,0.00001619228],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949769,0.00020229,0.002402009,0.00004232076,0.000006697407,0.000004638308,0.001233719,0.00003494486,0.001096396],"genre_scores_gemma":[0.9985044,0.00002264185,0.000505412,0.000007114063,0.000003962013,0.000003189134,0.0008826411,0.000003233738,0.00006745048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03656092,"threshold_uncertainty_score":0.07269621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007096365062789351,"score_gpt":0.1769230615023468,"score_spread":0.1698266964395575,"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."}}