{"id":"W6901935439","doi":"10.6084/m9.figshare.12257744","title":"Additional file 1 of Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Artificial neural network; Table (database); Soil nutrients; China","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003052923,0.00009158278,0.0001894201,0.00002572585,0.00006728251,0.00001592002,0.0003077338,0.00005442475,0.7444836],"category_scores_gemma":[0.001753543,0.00008621791,0.00003324477,0.0003376931,0.0001227946,0.0001409565,0.0005699072,0.0002209473,0.00009695112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006018667,"about_ca_system_score_gemma":0.00002389469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004087884,"about_ca_topic_score_gemma":0.00002899633,"domain_scores_codex":[0.9989113,0.0001090795,0.0002820013,0.0002641419,0.000274671,0.0001588152],"domain_scores_gemma":[0.9993918,0.0002189323,0.0002412075,0.00008671669,0.000009947879,0.00005138333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005691455,0.001427841,0.05882305,0.0001032204,0.00005371098,0.00008031237,0.007067002,0.1661426,0.3517905,0.000004376551,0.123799,0.2901392],"study_design_scores_gemma":[0.0006865795,0.001193036,0.01638366,0.0007018589,0.00002300172,0.00001995995,0.0001097546,0.8016599,0.09030027,0.0002441014,0.08822281,0.0004550368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752837,0.000002757275,0.00002473245,0.00007040479,0.00001005766,0.0002120301,0.009933326,0.000004031315,0.01445897],"genre_scores_gemma":[0.8649344,3.319077e-7,0.1328347,0.0000357076,0.00002184253,0.00002067044,0.001870986,0.00001333487,0.0002680582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7443867,"threshold_uncertainty_score":0.3515865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06051189985831263,"score_gpt":0.3148566227657805,"score_spread":0.2543447229074679,"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."}}