{"id":"W2997275685","doi":"","title":"Using Fuzzy-Set Classification to Analyse Sea-Level Indicators With Respect to Glacial-Isostatic Adjustment","year":2004,"lang":"en","type":"article","venue":"Publication Database GFZ (GFZ German Research Centre for Geosciences)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Post-glacial rebound; Glacial period; Geology; Sea level; Set (abstract data type); Physical geography; Computer science; Geomorphology; Geography; Oceanography","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":[],"consensus_categories":[],"category_scores_codex":[0.002574819,0.0003552921,0.0004969022,0.004508799,0.0005587147,0.001547339,0.000599805,0.000475565,0.001514289],"category_scores_gemma":[0.00675915,0.0001490463,0.0006560551,0.003030189,0.0005995175,0.0009229722,0.0003228468,0.0003770149,0.000295172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009678984,"about_ca_system_score_gemma":0.0007099454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01310815,"about_ca_topic_score_gemma":0.008010914,"domain_scores_codex":[0.9991735,0.0001837915,0.0001570643,0.0001294336,0.0002804478,0.00007563288],"domain_scores_gemma":[0.9964753,0.00238559,0.0002567561,0.0001998884,0.0006022698,0.00008023367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008035787,0.0002742044,0.06868181,0.0003357739,0.000279285,0.0003573554,0.001374065,0.1851903,0.02102302,0.02004153,0.002100452,0.6995387],"study_design_scores_gemma":[0.00004679961,0.000125016,0.02625264,0.00006182701,0.0001141236,0.0001191492,0.0003745313,0.9442322,0.01042574,0.01558191,0.002599961,0.00006606951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3131051,0.0002564614,0.6791641,0.0001837298,0.0000496931,0.0002103536,0.001236499,0.0008750207,0.004919015],"genre_scores_gemma":[0.6317379,0.00009834756,0.3659559,0.00003411325,0.00002776855,0.0001489219,0.001085022,0.00002002059,0.0008919579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01310815,"threshold_uncertainty_score":0.02606374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1314862769306171,"score_gpt":0.3843150140032893,"score_spread":0.2528287370726722,"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."}}