{"id":"W6908893992","doi":"10.3389/frwa.2022.803869.s004","title":"Table_4_Navigating Great Lakes Hydroclimate Data.pdf","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"German Social Sciences and History","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Natural (archaeology); Current (fluid); Channel (broadcasting); Surface water; Work (physics); Hydrological modelling; Climate change; Limnology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007109488,0.0005012619,0.0003460576,0.002703714,0.0007364998,0.002590357,0.001055899,0.0005471386,0.4691258],"category_scores_gemma":[0.004739584,0.000483569,0.0004335679,0.005803746,0.000246788,0.001803766,0.001641834,0.0008368121,0.2094809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007393006,"about_ca_system_score_gemma":0.001836039,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02787005,"about_ca_topic_score_gemma":0.03462256,"domain_scores_codex":[0.9996289,0.0000475489,0.00003875458,0.00006668946,0.0001791317,0.00003898534],"domain_scores_gemma":[0.9968966,0.000808168,0.0002126124,0.0003841379,0.001341233,0.0003572942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001399683,0.000008245046,0.001215581,0.0002043879,0.000005601791,0.0000367904,0.00004587784,0.0001924571,0.000117931,0.0007846322,0.9772528,0.02012164],"study_design_scores_gemma":[0.00002015485,0.000005495728,0.003875074,0.0000924909,0.000002515753,0.00002575937,0.00005540218,0.0001850166,0.0002221367,0.0006661955,0.9948372,0.00001259639],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008925857,0.0001691529,0.001601606,0.0007928562,0.0002463852,0.0001809244,0.8948555,0.005922947,0.09533804],"genre_scores_gemma":[0.01167598,0.0008682236,0.01413069,0.001350696,0.0002130908,0.0007439573,0.8896192,0.004837965,0.07656021],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9721299,"threshold_uncertainty_score":0.7572275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09167411462211333,"score_gpt":0.3440789227684325,"score_spread":0.2524048081463192,"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."}}