{"id":"W6945901440","doi":"10.26023/hy1t-thvn-zd0y","title":"WINTRE-MIX: UQAM-PK GEONOR Precipitation Gauge data. Version 1.0","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Precipitation; Rain gauge; Gauge (firearms); Calibration; Raw data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008772697,0.002225295,0.001203929,0.003405043,0.0009144908,0.001833343,0.002848602,0.001533263,0.03396069],"category_scores_gemma":[0.004646795,0.0006527745,0.001020811,0.007632752,0.0004447966,0.001070902,0.001596514,0.001640429,0.07195073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002496215,"about_ca_system_score_gemma":0.004535865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2180487,"about_ca_topic_score_gemma":0.3303721,"domain_scores_codex":[0.9989941,0.0001243882,0.0001111341,0.0002320306,0.0003082269,0.0002302199],"domain_scores_gemma":[0.9975033,0.0003298647,0.0001839938,0.000500474,0.001197735,0.0002847362],"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":[0.00002789252,0.00001250653,0.0007378364,0.0001902695,0.00001343289,0.00001472934,0.00001671555,0.0002298725,0.00007019519,0.0002022253,0.997131,0.001353328],"study_design_scores_gemma":[0.000176217,0.00001336363,0.008201589,0.0001992338,0.00001903721,0.00004254269,0.00009300467,0.000841444,0.0004362141,0.0007974184,0.9891369,0.00004294375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001103611,0.0000222291,0.00004207422,0.00002911432,0.00001446267,0.000008161121,0.9990919,0.0003304449,0.0003513176],"genre_scores_gemma":[0.0002446036,0.00001872173,0.0001337957,0.0000165865,0.00000355372,0.00002764568,0.9991921,0.00005617925,0.0003069198],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2180487,"threshold_uncertainty_score":0.433559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2156514467390361,"score_gpt":0.4347120097283749,"score_spread":0.2190605629893387,"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."}}