{"id":"W6887974161","doi":"10.18164/d68361d0-8141-48b9-a25e-a9bc98d71438","title":"d4PDF-WaveHs: the first SMILE-based ensemble of global historical wave height","year":2022,"lang":"en","type":"dataset","venue":"ECCC Data Catalogue","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Government of Quebec","funders":"","keywords":"Climate change; Wave height; Sampling (signal processing); Significant wave height; Submarine pipeline; Extreme value theory; Sea level; Climate model; Statistical model","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006164383,0.0004220325,0.0005090107,0.000173401,0.0004492724,0.0002304012,0.002297926,0.000181235,0.006176455],"category_scores_gemma":[0.0002022662,0.0003418094,0.0001563172,0.000797953,0.0001094886,0.0007282455,0.002138468,0.0004546136,0.0004096941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006458298,"about_ca_system_score_gemma":0.0001657003,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02279949,"about_ca_topic_score_gemma":0.01565079,"domain_scores_codex":[0.9973572,0.00003464865,0.0006330204,0.0007683557,0.0008080784,0.0003986504],"domain_scores_gemma":[0.99635,0.000127241,0.0007166223,0.002687194,0.00009452122,0.00002440921],"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":[0.00007115568,0.0002172821,0.0001172453,0.0004484072,0.00006942364,0.0000669333,0.00000661589,0.0000173032,0.000001432795,0.0001188289,0.9981231,0.0007422573],"study_design_scores_gemma":[0.0005916447,0.00001733278,0.0002907441,0.00004106008,0.0003406919,0.000008494493,0.00005444195,0.0003224559,0.000002595826,0.0000792717,0.9978741,0.0003770996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006380434,0.0006505944,0.0000763703,0.001788493,0.002355028,0.0004541761,0.9941818,0.00005746107,0.0003723034],"genre_scores_gemma":[0.0007789042,0.00006404782,0.00002675243,0.003117059,0.001279779,0.00005735942,0.9945633,0.00003076826,0.0000819964],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007148698,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05578616851439845,"score_gpt":0.2459349467956126,"score_spread":0.1901487782812141,"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."}}