{"id":"W4394494568","doi":"10.6084/m9.figshare.22231429","title":"Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm)","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Ottawa","funders":"","keywords":"Plateau (mathematics); Layer (electronics); Geology; Resolution (logic); Moisture; Loess plateau; Water content; Soil science; Materials science; Computer science; Mathematics; Geotechnical engineering; Composite material; Artificial intelligence","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":[0.0004330191,0.001236593,0.0005789654,0.001382862,0.0003626281,0.0005349194,0.001693458,0.0006631817,0.01409693],"category_scores_gemma":[0.0008549006,0.0003700998,0.0008509273,0.002691815,0.0002718533,0.0005495389,0.000583296,0.000820286,0.01014854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008547539,"about_ca_system_score_gemma":0.001181208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07339057,"about_ca_topic_score_gemma":0.09192996,"domain_scores_codex":[0.9998056,0.00002053872,0.00002426716,0.00006575877,0.00004739101,0.00003630523],"domain_scores_gemma":[0.9996297,0.00003612417,0.00003533163,0.00008662845,0.000165629,0.00004662953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002260899,0.0001370592,0.01520462,0.0009874513,0.0001903425,0.0001521607,0.00009256324,0.009290443,0.00156915,0.0006164089,0.9540412,0.01749237],"study_design_scores_gemma":[0.0008536269,0.0001526253,0.2517081,0.000517189,0.0002146447,0.0002608071,0.0003118485,0.03590743,0.006700364,0.002378525,0.700789,0.0002059529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004351699,0.00003230818,0.0002356185,0.00004218811,0.00003837968,0.00002020131,0.994187,0.0005432884,0.0005493391],"genre_scores_gemma":[0.004735795,0.0000232561,0.0004545284,0.00001614791,0.000005572698,0.00004650472,0.9942491,0.00003797201,0.0004311662],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07339057,"threshold_uncertainty_score":0.1459268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08248975896275262,"score_gpt":0.2725430236369812,"score_spread":0.1900532646742286,"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."}}